Publications

MergeVLA: Cross-Skill Model Merging Toward a Generalist Vision-Language-Action Agent

Yuxia Fu, Zhizhen Zhang, Yuqi Zhang, Zijian Wang, and 2 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CORE A*

Bib

@inproceedings{fu2026mergevla,
  author = {Fu, Yuxia and Zhang, Zhizhen and Zhang, Yuqi and Wang, Zijian and Huang, Zi and Luo, Yadan†},
  title = {MergeVLA: Cross-Skill Model Merging Toward a Generalist Vision-Language-Action Agent},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  abbr = {CVPR},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {embodied}
}

TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction

Fengyi Zhang, Tianjun Zhang, Kasra Khosoussi, Zheng Zhang, and 2 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CORE A*

Bib

@inproceedings{zhang2026talo,
  author = {Zhang, Fengyi and Zhang, Tianjun and Khosoussi, Kasra and Zhang, Zheng and Huang, Zi and Luo, Yadan†},
  title = {TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  abbr = {CVPR},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {autonomous-driving}
}

Test-Time 3D Occupancy Prediction

Fengyi Zhang, Xiangyu Sun, Huitong Yang, Zheng Zhang, and 2 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CORE A*

Bib

@inproceedings{zhang2026ttocc,
  author = {Zhang, Fengyi and Sun, Xiangyu and Yang, Huitong and Zhang, Zheng and Huang, Zi and Luo, Yadan†},
  title = {Test-Time 3D Occupancy Prediction},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  abbr = {CVPR},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {autonomous-driving}
}

GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Driving

Lin Liu, Caiyan Jia, Guanyi Yu, Ziying Song, and 5 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CORE A*

🏆 Spotlight (top 3%) Awarded Bib

Spotlight (top 3%)

@inproceedings{liu2026guideflow,
  author = {Liu, Lin and Jia, Caiyan and Yu, Guanyi and Song, Ziying and Li, Junqiao and Jia, Feiyang and Wu, Peiliang and Hao, Xiaoshuai and Luo, Yadan},
  title = {GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Driving},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  abbr = {CVPR},
  award = {Spotlight (top 3%)},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {autonomous-driving}
}

OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation

Zhuoxiao Chen, Hongyang Yu, Ying Xu, Yadan Luo, and 2 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CORE A*

Bib

@inproceedings{chen2026orapo,
  author = {Chen, Zhuoxiao and Yu, Hongyang and Xu, Ying and Luo, Yadan and Duong, Long and Li, Yuan-Fang},
  title = {OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  abbr = {CVPR},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {adaptation}
}

Language-driven Fine-grained Retrieval

Shijie Wang, Xin Yu, Yadan Luo, Zijian Wang, and 2 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CORE A*

Bib

@inproceedings{wang2026lfdretrieval,
  author = {Wang, Shijie and Yu, Xin and Luo, Yadan and Wang, Zijian and Zhang, Peng-Fei and Huang, Zi},
  title = {Language-driven Fine-grained Retrieval},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  abbr = {CVPR},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {adaptation}
}

ICML

Norm×Direction: Restoring the Missing Query Norm in Vision Linear Attention

Weikang Meng, Yadan Luo, Liangyu Huo, Yingjian Li, and 3 more authors

International Conference on Machine Learning (ICML), 2026

CORE A*

Bib

@article{meng2026normdirection,
  title = {Norm×Direction: Restoring the Missing Query Norm in Vision Linear Attention},
  author = {Meng, Weikang and Luo, Yadan and Huo, Liangyu and Li, Yingjian and Wang, Yaowei and Li, Xin and Zhang, Zheng},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2026},
  abbr = {ICML},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {efficiency}
}

ICML

Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models

Yuanwei Hu, Bo Peng, Yadan Luo, Jie Lu, and 2 more authors

International Conference on Machine Learning (ICML), 2026

CORE A*

Bib

@article{hu2026modalitygap,
  title = {Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models},
  author = {Hu, Yuanwei and Peng, Bo and Luo, Yadan and Lu, Jie and Chen, Ling and Fang, Zhen},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2026},
  abbr = {ICML},
  note = {CORE A*},
  bibtex_show = {true},
  topic = {adaptation}
}

Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents

Zhizhen Zhang, Lei Zhu, Zhen Fang, Zi Huang, and 1 more author

Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

arXiv Bib Website

@article{zhang2025actol,
  title = {Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents},
  author = {Zhang, Zhizhen and Zhu, Lei and Fang, Zhen and Huang, Zi and Luo, Yadan},
  journal = {Annual Conference on Neural Information Processing Systems (NeurIPS)},
  abbr = {NeurIPS},
  arxiv = {2502.01218},
  selected = {true},
  bibtex_show = {true},
  preview = {actol.png},
  website = {https://actol-pretrain.github.io/},
  year = {2025},
  topic = {embodied}
}

CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving

Huitong Yang, Zhuoxiao Chen, Fengyi Zhang, Zi Huang, and 1 more author

Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

arXiv Bib

@article{yang2025codemerge,
  title = {CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving},
  author = {Yang, Huitong and Chen, Zhuoxiao and Zhang, Fengyi and Huang, Zi and Luo, Yadan},
  journal = {Annual Conference on Neural Information Processing Systems (NeurIPS)},
  abbr = {NeurIPS},
  arxiv = {2505.16524},
  selected = {true},
  preview = {codemerge.png},
  bibtex_show = {true},
  year = {2025},
  topics = {autonomous-driving, adaptation}
}

Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving

Hossein Askari, Yadan Luo, Hongfu Sun, and Fred Roosta

Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

Bib

@article{hossein25LRF,
  title = {Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving},
  author = {Askari, Hossein and Luo, Yadan and Sun, Hongfu and Roosta, Fred},
  journal = {Annual Conference on Neural Information Processing Systems (NeurIPS)},
  abbr = {NeurIPS},
  preview = {lflow.png},
  bibtex_show = {true},
  year = {2025},
  topic = {adaptation}
}

Are Synthetic Videos Useful? A Benchmark for Retrieval-Centric Evaluation of Synthetic Videos

Zecheng Zhao, Selena Song, Tong Chen, Zhi Chen, and 2 more authors

ACM International Conference on Multimedia, MM, 2025

arXiv Bib Website

@article{zhao2025synthetic,
  title = {Are Synthetic Videos Useful? A Benchmark for Retrieval-Centric Evaluation of Synthetic Videos},
  author = {Zhao, Zecheng and Song, Selena and Chen, Tong and Chen, Zhi and Sadiq, Shazia and Luo, Yadan},
  arxiv = {2507.02316},
  journal = {{ACM} International Conference on Multimedia,
                    {MM}},
  website = {https://jasoncodemaker.github.io/SynTVA/},
  bibtex_show = {true},
  preview = {synta.png},
  year = {2025},
  abbr = {MM},
  topic = {adaptation}
}

WisWheat: A Three-Tiered Vision-Language Dataset for Wheat Management

Bowen Yuan, Selena Song, Javier Fernandez, Yadan Luo, and 2 more authors

ACM International Conference on Multimedia, MM, 2025

arXiv Bib

@article{yuan2025wiswheat,
  title = {WisWheat: A Three-Tiered Vision-Language Dataset for Wheat Management},
  author = {Yuan, Bowen and Song, Selena and Fernandez, Javier and Luo, Yadan and Baktashmotlagh, Mahsa and Wang, Zijian},
  arxiv = {2506.06084},
  journal = {{ACM} International Conference on Multimedia,
                    {MM}},
  bibtex_show = {true},
  preview = {wiswheat.png},
  year = {2025},
  abbr = {MM},
  topic = {adaptation}
}

Is Less More? Exploring Token Condensation as Training-free Adaptation for CLIP

Zixin Wang, Dong Gong, Sen Wang, Zi Huang, and 1 more author

In IEEE/CVF International Conference on Computer Vision, (ICCV), 2025

arXiv Bib PDF Code

@inproceedings{wang2025tca,
  author = {Wang, Zixin and Gong, Dong and Wang, Sen and Huang, Zi and Luo, Yadan},
  title = {Is Less More? Exploring Token Condensation as Training-free Adaptation for CLIP},
  booktitle = {{IEEE/CVF} International Conference on Computer Vision, {(ICCV)}},
  arxiv = {2410.14729},
  preview = {TCA.png},
  selected = {true},
  code = {https://github.com/Jo-wang/TCA},
  bibtex_show = {true},
  pdf = {https://arxiv.org/pdf/2410.14729},
  year = {2025},
  abbr = {ICCV},
  topic = {efficiency}
}

TPAMI

Open-CRB: Toward Open World Active Learning for 3D Object Detection

Zhuoxiao Chen, Yadan Luo, Zixin Wang, Zijian Wang, and 1 more author

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

@article{chen2025open,
  title = {Open-CRB: Toward Open World Active Learning for 3D Object Detection},
  author = {Chen, Zhuoxiao and Luo, Yadan and Wang, Zixin and Wang, Zijian and Huang, Zi},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {47},
  number = {10},
  pages = {8336--8350},
  year = {2025},
  publisher = {IEEE},
  preview = {opencrb.png},
  abbr = {TPAMI},
  bibtex_show = {true},
  arxiv = {2503.15930},
  code = {https://github.com/Luoyadan/CRB-active-3Ddet/tree/Open-CRB},
  topic = {autonomous-driving}
}

Don’t Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving

Ziying Song, Caiyan Jia, Lin Liu, Hongyu Pan, and 6 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

arXiv Bib PDF Code

@inproceedings{song2025momad,
  author = {Song, Ziying and Jia, Caiyan and Liu, Lin and Pan, Hongyu and Zhang, Yongchang and Wang, Junming and Zhang, Xingyu and Xu, Shaoqing and Yang, Lei and Luo, Yadan},
  title = {Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  arxiv = {2503.03125},
  preview = {momad.png},
  selected = {true},
  code = {https://github.com/adept-thu/MomAD},
  bibtex_show = {true},
  pdf = {https://arxiv.org/pdf/2503.03125},
  year = {2025},
  abbr = {CVPR},
  topic = {autonomous-driving}
}

MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection

Zhuoxiao Chen, Junjie Meng, Mahsa Baktashmotlagh, Yonggang Zhang, and 2 more authors

In International Conference on Learning Representations (ICLR), 2025

🏆 Oral arXiv Bib PDF Code

@inproceedings{DBLP:conf/iclr/mos,
  author = {Chen, Zhuoxiao and Meng, Junjie and Baktashmotlagh, Mahsa and Zhang, Yonggang and Huang, Zi and Luo, Yadan},
  title = {MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection},
  booktitle = {International Conference on Learning Representations ({ICLR})},
  oral = {true},
  publisher = {OpenReview.net},
  arxiv = {2406.14878},
  preview = {mos.png},
  selected = {true},
  code = {https://github.com/zhuoxiao-chen/MOS},
  bibtex_show = {true},
  pdf = {https://openreview.net/forum?id=Y6aHdDNQYD},
  year = {2025},
  abbr = {ICLR},
  topic = {autonomous-driving}
}

PolaFormer: Polarity-aware Linear Attention for Vision Transformers

Weikang Meng, Yadan Luo, Xin Li, Dongmei Jiang, and 1 more author

In International Conference on Learning Representations (ICLR), 2025

arXiv Bib PDF Code

@inproceedings{DBLP:conf/iclr/polaformer,
  author = {Meng, Weikang and Luo, Yadan and Li, Xin and Jiang, Dongmei and Zhang, Zheng},
  title = {PolaFormer: Polarity-aware Linear Attention for Vision Transformers},
  booktitle = {International Conference on Learning Representations ({ICLR})},
  publisher = {OpenReview.net},
  arxiv = {2501.15061},
  preview = {polaformer.png},
  selected = {true},
  code = {https://github.com/ZacharyMeng/PolaFormer},
  bibtex_show = {true},
  pdf = {https://openreview.net/forum?id=kN6MFmKUSK},
  year = {2025},
  abbr = {ICLR},
  topic = {efficiency}
}

Nature Comm

Prediction of Perovskite Oxygen Vacancies for Oxygen Electrocatalysis at Different Temperatures

Zhiheng Li, Xin Mao, Desheng Feng, Mengran Li, and 7 more authors

Nature Communications, 2024

Bib PDF Code

@article{li2024prediction,
  title = {Prediction of Perovskite Oxygen Vacancies for Oxygen Electrocatalysis at Different Temperatures},
  author = {Li, Zhiheng and Mao, Xin and Feng, Desheng and Li, Mengran and Xu, Xiaoyong and Luo, Yadan and Zhuang, Linzhou and Lin, Rijia and Zhu, Tianjiu and Liang, Fengli and others},
  journal = {Nature Communications},
  volume = {15},
  number = {1},
  pdf = {https://www.nature.com/articles/s41467-024-53578-7.pdf},
  code = {https://github.com/Luoyadan/MLtoolbox-perovskite-oxygen-vacancy-pred},
  preview = {nc.PNG},
  pages = {9318},
  bibtex_show = {true},
  year = {2024},
  publisher = {Nature Publishing Group UK London},
  abbr = {Nature Comm},
  topic = {other}
}

DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection

Jia Syuen Lim, Zhuoxiao Chen, Mahsa Baktashmotlagh, Zhi Chen, and 3 more authors

Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

@article{DBLP:journals/corr/abs-2406-14924,
  author = {Lim, Jia Syuen and Chen, Zhuoxiao and Baktashmotlagh, Mahsa and Chen, Zhi and Yu, Xin and Huang, Zi and Luo, Yadan},
  title = {DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection},
  journal = {Annual Conference on Neural Information Processing Systems (NeurIPS)},
  abbr = {NeurIPS},
  volume = {abs/2406.14924},
  year = {2024},
  arxiv = {2406.14924},
  pdf = {https://openreview.net/pdf/af3d8187b35b135bc5f58bdbabeea0da01477f2a.pdf},
  doi = {10.48550/ARXIV.2406.14924},
  eprinttype = {arXiv},
  code = {https://github.com/jason-lim26/DiPEx},
  preview = {dipex.PNG},
  eprint = {2406.14924},
  timestamp = {Sun, 06 Oct 2024 21:25:19 +0200},
  bibtex_show = {true},
  biburl = {https://dblp.org/rec/journals/corr/abs-2406-14924.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  topic = {adaptation}
}

Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels

Haoyang Luo, Zheng Zhang, and Yadan Luo

Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

Bib Code

@article{luoexploiting,
  author = {Luo, Haoyang and Zhang, Zheng and Luo, Yadan},
  title = {Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels},
  journal = {Annual Conference on Neural Information Processing Systems (NeurIPS)},
  abbr = {NeurIPS},
  year = {2024},
  code = {https://github.com/E-Galois/PCRIL},
  preview = {pcirl.PNG},
  paper = {https://openreview.net/pdf?id=ferj6WqShv},
  url = {https://neurips.cc/virtual/2024/poster/94194},
  bibtex_show = {true},
  topic = {adaptation}
}

Color-Oriented Redundancy Reduction in Dataset Distillation

Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh, Yadan Luo, and 1 more author

Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

@article{yuan2024color,
  author = {Yuan, Bowen and Wang, Zijian and Baktashmotlagh, Mahsa and Luo, Yadan and Huang, Zi},
  title = {Color-Oriented Redundancy Reduction in Dataset Distillation},
  journal = {Annual Conference on Neural Information Processing Systems (NeurIPS)},
  abbr = {NeurIPS},
  year = {2024},
  arxiv = {2411.11329},
  eprinttype = {arXiv},
  code = {https://github.com/kevinyuan0314/autopalette},
  preview = {crr.PNG},
  eprint = {2406.14924},
  timestamp = {Sun, 06 Oct 2024 21:25:19 +0200},
  bibtex_show = {true},
  biburl = {https://dblp.org/rec/journals/corr/abs-2406-14924.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  topic = {efficiency}
}

DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection

Zhuoxiao Chen, Zixin Wang, Sen Wang, Zi Huang, and 1 more author

ACM International Conference on Multimedia, MM, 2024

DOI arXiv Bib Code

@article{DBLP:journals/corr/abs-2406-13891,
  author = {Chen, Zhuoxiao and Wang, Zixin and Wang, Sen and Huang, Zi and Luo, Yadan},
  title = {{DPO:} Dual-Perturbation Optimization for Test-time Adaptation in
                    3D Object Detection},
  journal = {{ACM} International Conference on Multimedia,
                    {MM}},
  volume = {abs/2406.13891},
  year = {2024},
  preview = {dpo.PNG},
  abbr = {MM},
  arxiv = {2406.13891},
  url = {https://doi.org/10.48550/arXiv.2406.13891},
  doi = {10.48550/ARXIV.2406.13891},
  eprinttype = {arXiv},
  code = {https://github.com/Jo-wang/DPO},
  eprint = {2406.13891},
  timestamp = {Fri, 12 Jul 2024 19:38:49 +0200},
  bibtex_show = {true},
  biburl = {https://dblp.org/rec/journals/corr/abs-2406-13891.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  topic = {autonomous-driving}
}

ECCV

Find n’ Propagate: Open-Vocabulary 3D Object Detection in Urban Environments

Djamahl Etchegaray, Zi Huang, Tatsuya Harada, and Yadan Luo

European Conference on Computer Vision (ECCV), 2024

DOI arXiv Bib Code

@article{DBLP:journals/corr/abs-2403-13556,
  author = {Etchegaray, Djamahl and Huang, Zi and Harada, Tatsuya and Luo, Yadan},
  title = {Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments},
  journal = {European Conference on Computer Vision (ECCV)},
  volume = {abs/2403.13556},
  year = {2024},
  code = {https://github.com/djamahl99/findnpropagate},
  arxiv = {https://doi.org/10.48550/arXiv.2403.13556},
  doi = {10.48550/ARXIV.2403.13556},
  eprinttype = {arXiv},
  abbr = {ECCV},
  preview = {teaser-OV3D-dj.png},
  eprint = {2403.13556},
  timestamp = {Mon, 08 Apr 2024 18:24:51 +0200},
  bibtex_show = {true},
  biburl = {https://dblp.org/rec/journals/corr/abs-2403-13556.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  topic = {autonomous-driving}
}

IJCV

In Search of Lost Online Test-time Adaptation: A Survey

Zixin Wang, Yadan Luo, Liang Zheng, Zhuoxiao Chen, and 2 more authors

International Journal of Computer Vision (IJCV), 2024

DOI arXiv Bib Code

@article{DBLP:journals/corr/abs-2310-20199,
  author = {Wang, Zixin and Luo, Yadan and Zheng, Liang and Chen, Zhuoxiao and Wang, Sen and Huang, Zi},
  title = {In Search of Lost Online Test-time Adaptation: {A} Survey},
  journal = {International Journal of Computer Vision (IJCV)},
  volume = {abs/2310.20199},
  year = {2024},
  url = {https://doi.org/10.48550/arXiv.2310.20199},
  doi = {10.48550/ARXIV.2310.20199},
  arxiv = {https://arxiv.org/pdf/2310.20199},
  preview = {otta.png},
  abbr = {IJCV},
  code = {https://github.com/Jo-wang/OTTA_ViT_survey},
  eprinttype = {arXiv},
  eprint = {2310.20199},
  timestamp = {Tue, 07 May 2024 20:16:14 +0200},
  bibtex_show = {true},
  biburl = {https://dblp.org/rec/journals/corr/abs-2310-20199.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  topic = {adaptation}
}

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection

Bo Peng*, Yadan Luo*, Yonggang Zhang, Yixuan Li, and 1 more author

In International Conference on Learning Representations (ICLR), 2024

arXiv Bib

@inproceedings{DBLP:conf/iclr/conjnorm,
  author = {Peng*, Bo and Luo*, Yadan and Zhang, Yonggang and Li, Yixuan and Fang, Zhen},
  title = {ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection},
  booktitle = {International Conference on Learning Representations (ICLR)},
  publisher = {OpenReview.net},
  arxiv = {https://arxiv.org/abs/2402.17888},
  preview = {conjnorm.png},
  selected = {true},
  bibtex_show = {true},
  year = {2024},
  abbr = {ICLR},
  topic = {adaptation}
}

MM

Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error

Zixin Wang, Yadan Luo, Zhi Chen, Sen Wang, and 1 more author

In ACM International Conference on Multimedia, MM, 2023

🏆 Best Student Paper Award Awarded DOI arXiv Code

Best Student Paper Award

KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection

Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, and 2 more authors

In 2023 IEEE/CVF International Conference on Computer Vision, ICCV, 2023

@inproceedings{luo2023kecor,
  author = {Luo, Yadan and Chen, Zhuoxiao and Fang, Zhen and and Zheng Zhang and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection},
  booktitle = {2023 {IEEE/CVF} International Conference on Computer Vision, {ICCV}},
  publisher = {{IEEE}},
  year = {2023},
  preview = {kecor.png},
  selected = {false},
  code = {https://github.com/Luoyadan/KECOR-active-3Ddet},
  arxiv = {https://arxiv.org/abs/2307.07942},
  bibtex_show = {true},
  abbr = {ICCV},
  topic = {autonomous-driving}
}

Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling

Zhuoxiao Chen, Yadan Luo, Zi Huang, and Mahsa Baktashmotlagh

In 2023 IEEE/CVF International Conference on Computer Vision, ICCV, 2023

@inproceedings{chen2023revisit3d,
  author = {Chen, Zhuoxiao and Luo, Yadan and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling},
  booktitle = {2023 {IEEE/CVF} International Conference on Computer Vision, {ICCV}},
  publisher = {{IEEE}},
  year = {2023},
  preview = {redb.png},
  arxiv = {https://arxiv.org/abs/2307.07944},
  code = {https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet},
  bibtex_show = {true},
  abbr = {ICCV},
  topic = {autonomous-driving}
}

How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Transferability

Zijian Wang, Yadan Luo, Liang Zheng, Zi Huang, and 1 more author

In 2023 IEEE/CVF International Conference on Computer Vision, ICCV, 2023

@inproceedings{wang2023ncti,
  author = {Wang, Zijian and Luo, Yadan and Zheng, Liang and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Transferability},
  booktitle = {2023 {IEEE/CVF} International Conference on Computer Vision, {ICCV}},
  publisher = {{IEEE}},
  arxiv = {https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_How_Far_Pre-trained_Models_Are_from_Neural_Collapse_on_the_ICCV_2023_paper.pdf},
  year = {2023},
  preview = {ncti.png},
  code = {https://github.com/BUserName/NCTI},
  bibtex_show = {true},
  abbr = {ICCV},
  topic = {adaptation}
}

ICDM

Learning Efficient Unsupervised Satellite Image-based Building Damage Detection

Yiyun Zhang, Zijian Wang, Yadan Luo, Xin Yu, and 1 more author

In 2023 IEEE International Conference on Data Mining (ICDM), 2023

Bib

@inproceedings{zhang2023learning,
  title = {Learning Efficient Unsupervised Satellite Image-based Building Damage Detection},
  author = {Zhang, Yiyun and Wang, Zijian and Luo, Yadan and Yu, Xin and Huang, Zi},
  booktitle = {2023 IEEE International Conference on Data Mining (ICDM)},
  pages = {1547--1552},
  preview = {damagedet.png},
  year = {2023},
  bibtex_show = {true},
  organization = {IEEE},
  abbr = {ICDM},
  topic = {adaptation}
}

PR

Deep Collaborative Graph Hashing for Discriminative Image Retrieval

Zheng Zhang, Jianning Wang, Lei Zhu, Yadan Luo, and 1 more author

Pattern Recognition, 2023

DOI arXiv Code
PR

Hypercomplex context guided interaction modeling for scene graph generation

Zheng Wang, Xing Xu, Yadan Luo, Guoqing Wang, and 1 more author

Pattern Recognition, 2023

DOI arXiv

TPAMI

Source-free Progressive Graph Learning for Open-set Domain Adaptation

Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, and 1 more author

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

@article{luo2022source,
  title = {Source-free Progressive Graph Learning for Open-set Domain Adaptation},
  author = {Luo, Yadan and Wang, Zijian and Chen, Zhuoxiao and Huang, Zi and Baktashmotlagh, Mahsa},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  arxiv = {https://arxiv.org/abs/2202.06174},
  code = {https://github.com/Luoyadan/SF-PGL},
  preview = {sfpgl.png},
  bibtex_show = {true},
  year = {2023},
  abbr = {TPAMI},
  topic = {adaptation}
}

Exploring Active 3D Object Detection from a Generalization Perspective

Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, and 2 more authors

In International Conference on Learning Representations, ICLR 2023, 2023

@inproceedings{DBLP:conf/iclr/crb,
  author = {Luo, Yadan and Chen, Zhuoxiao and Wang, Zijian and Yu, Xin and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {Exploring Active 3D Object Detection from a Generalization Perspective},
  booktitle = {International Conference on Learning Representations, {ICLR} 2023},
  publisher = {OpenReview.net},
  arxiv = {https://arxiv.org/abs/2301.09249},
  code = {https://github.com/Luoyadan/CRB-active-3Ddet},
  preview = {crb.png},
  selected = {false},
  bibtex_show = {true},
  year = {2023},
  abbr = {ICLR},
  topic = {autonomous-driving}
}

Learning to Diversify for Single Domain Generalization

Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, and 1 more author

In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, 2021

@inproceedings{DBLP:conf/iccv/WangLQHB21,
  author = {Wang, Zijian and Luo, Yadan and Qiu, Ruihong and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {Learning to Diversify for Single Domain Generalization},
  booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV}
                 2021, Montreal, QC, Canada, October 10-17, 2021},
  pages = {814--823},
  publisher = {{IEEE}},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/ICCV48922.2021.00087},
  doi = {10.1109/ICCV48922.2021.00087},
  timestamp = {Thu, 23 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/iccv/WangLQHB21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  code = {https://github.com/BUserName/Learning_to_diversify},
  arxiv = {http://arxiv.org/abs/2108.11726},
  pdf = {https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Learning_To_Diversify_for_Single_Domain_Generalization_ICCV_2021_paper.pdf},
  preview = {L2D.png},
  abbr = {ICCV},
  topic = {adaptation}
}

Semantics Disentangling for Generalized Zero-Shot Learning

Zhi Chen, Yadan Luo, Ruihong Qiu, Sen Wang, and 3 more authors

In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, 2021

@inproceedings{DBLP:conf/iccv/ChenLQ0H0021,
  author = {Chen, Zhi and Luo, Yadan and Qiu, Ruihong and Wang, Sen and Huang, Zi and Li, Jingjing and Zhang, Zheng},
  title = {Semantics Disentangling for Generalized Zero-Shot Learning},
  booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV}
                 2021, Montreal, QC, Canada, October 10-17, 2021},
  pages = {8692--8700},
  publisher = {{IEEE}},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/ICCV48922.2021.00859},
  doi = {10.1109/ICCV48922.2021.00859},
  timestamp = {Thu, 23 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/iccv/ChenLQ0H0021.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Semantics_Disentangling_for_Generalized_Zero-Shot_Learning_ICCV_2021_paper.pdf},
  arxiv = {https://arxiv.org/abs/2101.07978},
  code = {https://github.com/uqzhichen/SDGZSL},
  preview = {sdgzsl.png},
  abbr = {ICCV},
  topic = {adaptation}
}

TKDE

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, and 2 more authors

IEEE Transactions on Knowledge and Data Engineering, 2021

DOI Bib Code

@article{luo2021interpretable,
  title = {Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph},
  author = {Luo, Yadan and Huang, Zi and Chen, Hongxu and Yang, Yang and Ying, Hongzhi and Baktashmotlagh, Mahsa},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2021},
  publisher = {IEEE},
  doi = {10.1109/TKDE.2021.3139035},
  bibtex_show = {true},
  code = {https://github.com/Luoyadan/SIHG},
  preview = {SHIG.png},
  abbr = {TKDE},
  topic = {adaptation}
}

WACV

FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification

Zijian Wang, Yadan Luo, Zi Huang, and Mahsa Baktashmotlagh

In IEEE Winter Conference on Applications of Computer Vision, WACV 2023, 2023

Bib

@inproceedings{newwacv,
  author = {Wang, Zijian and Luo, Yadan and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification},
  booktitle = {{IEEE} Winter Conference on Applications of Computer Vision, {WACV}
                 2023},
  publisher = {{IEEE}},
  year = {2023},
  bibtex_show = {true},
  abbr = {WACV},
  topic = {adaptation}
}

TMM

GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning

Zhi Chen, Yadan Luo, Sen Wang, Jingjing Li, and 1 more author

IEEE Transactions on Multimedia, 2022

DOI Bib PDF

@article{9830066,
  author = {Chen, Zhi and Luo, Yadan and Wang, Sen and Li, Jingjing and Huang, Zi},
  journal = {IEEE Transactions on Multimedia},
  title = {GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning},
  year = {2022},
  volume = {},
  number = {},
  pages = {1-12},
  doi = {10.1109/TMM.2022.3190678},
  publisher = {IEEE},
  bibtex_show = {true},
  pdf = {https://ieeexplore.ieee.org/document/9830066},
  preview = {GZSL.png},
  abbr = {TMM},
  topic = {adaptation}
}

ICME

Discovering Domain Disentanglement for Generalized Multi-Source Domain Adaptation

Zixin Wang, Yadan Luo, Peng-Fei Zhang, Sen Wang, and 1 more author

In IEEE International Conference on Multimedia and Expo, ICME 2022, 2022

@inproceedings{DBLP:conf/icmcs/WangLZ0H22,
  author = {Wang, Zixin and Luo, Yadan and Zhang, Peng{-}Fei and Wang, Sen and Huang, Zi},
  title = {Discovering Domain Disentanglement for Generalized Multi-Source Domain
                 Adaptation},
  booktitle = {{IEEE} International Conference on Multimedia and Expo, {ICME} 2022},
  pages = {1--6},
  publisher = {{IEEE}},
  year = {2022},
  url = {https://doi.org/10.1109/ICME52920.2022.9859733},
  doi = {10.1109/ICME52920.2022.9859733},
  timestamp = {Wed, 31 Aug 2022 09:04:57 +0200},
  biburl = {https://dblp.org/rec/conf/icmcs/WangLZ0H22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  code = {https://github.com/Jo-wang/VDD},
  arxiv = {https://arxiv.org/abs/2207.05070?context=cs.AI},
  preview = {Zixin-icme.png},
  pdf = {https://ieeexplore.ieee.org/document/9859733/},
  bibtex_show = {true},
  abbr = {ICME},
  topic = {adaptation}
}

Point to Rectangle Matching for Image Text Retrieval

Zheng Wang, Zhenwei Gao, Xing Xu, Yadan Luo, and 2 more authors

In MM ’22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 - 14, 2022, 2022

DOI Bib PDF

@inproceedings{DBLP:conf/mm/WangGXLYS22,
  author = {Wang, Zheng and Gao, Zhenwei and Xu, Xing and Luo, Yadan and Yang, Yang and Shen, Heng Tao},
  editor = {Magalh{\~{a}}es, Jo{\~{a}}o and Bimbo, Alberto Del and Satoh, Shin'ichi and Sebe, Nicu and Alameda{-}Pineda, Xavier and Jin, Qin and Oria, Vincent and Toni, Laura},
  title = {Point to Rectangle Matching for Image Text Retrieval},
  booktitle = {{MM} '22: The 30th {ACM} International Conference on Multimedia, Lisboa,
                 Portugal, October 10 - 14, 2022},
  pages = {4977--4986},
  publisher = {{ACM}},
  year = {2022},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3503161.3548237},
  doi = {10.1145/3503161.3548237},
  timestamp = {Fri, 14 Oct 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mm/WangGXLYS22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/abs/10.1145/3503161.3548237},
  abbr = {MM},
  topic = {adaptation}
}

ICML

Progressive Graph Learning for Open-Set Domain Adaptation

Yadan Luo, Zijian Wang, Zi Huang, and Mahsa Baktashmotlagh

In Proc. of the 37th International Conference on Machine Learning, ICML 2020, 2020

Bib Code

@inproceedings{DBLP:conf/icml/LuoWHB20,
  author = {Luo, Yadan and Wang, Zijian and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {Progressive Graph Learning for Open-Set Domain Adaptation},
  booktitle = {Proc. of the 37th International Conference on Machine Learning,
                 {ICML} 2020},
  series = {Proceedings of Machine Learning Research},
  volume = {119},
  pages = {6468--6478},
  publisher = {{PMLR}},
  year = {2020},
  bibtex_show = {true},
  url = {http://proceedings.mlr.press/v119/luo20b.html},
  timestamp = {Tue, 15 Dec 2020 00:00:00 +0100},
  biburl = {https://dblp.org/rec/conf/icml/LuoWHB20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  selected = {false},
  code = {https://github.com/BUserName/PGL},
  preview = {PGL.png},
  abbr = {ICML},
  topic = {adaptation}
}

TKDE

Collaborative Learning for Extremely Low Bit Asymmetric Hashing

Yadan Luo, Zi Huang, Yang Li, Fumin Shen, and 2 more authors

IEEE Transactions on Knowledge and Data Engineering, 2020

@article{DBLP:journals/tkde/LuoHLSYC21,
  author = {Luo, Yadan and Huang, Zi and Li, Yang and Shen, Fumin and Yang, Yang and Cui, Peng},
  title = {Collaborative Learning for Extremely Low Bit Asymmetric Hashing},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  volume = {33},
  number = {12},
  pages = {3675--3685},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/TKDE.2020.2977633},
  doi = {10.1109/TKDE.2020.2977633},
  biburl = {https://dblp.org/rec/journals/tkde/LuoHLSYC21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  arxiv = {https://arxiv.org/pdf/1809.09329.pdf},
  code = {https://github.com/Luoyadan/MAH-Pytorch},
  pdf = {https://ieeexplore.ieee.org/document/9020183},
  preview = {mah.png},
  abbr = {TKDE},
  topic = {adaptation}
}

Deep Collaborative Discrete Hashing With Semantic-Invariant Structure Construction

Zijian Wang, Zheng Zhang, Yadan Luo, Zi Huang, and 1 more author

IEEE Transactions on Multimedia, 2021

@article{DBLP:journals/tmm/WangZLHS21,
  author = {Wang, Zijian and Zhang, Zheng and Luo, Yadan and Huang, Zi and Shen, Heng Tao},
  title = {Deep Collaborative Discrete Hashing With Semantic-Invariant Structure
                 Construction},
  journal = {{IEEE} Transactions on Multimedia},
  volume = {23},
  pages = {1274--1286},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/TMM.2020.2995267},
  doi = {10.1109/TMM.2020.2995267},
  timestamp = {Thu, 16 Sep 2021 18:02:47 +0200},
  biburl = {https://dblp.org/rec/journals/tmm/WangZLHS21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/10.1145/3331184.3331275},
  preview = {dcdh.png},
  code = {https://github.com/BUserName/DCDH},
  abbr = {ICCV},
  topic = {adaptation}
}

TNNLS

Inductive Structure Consistent Hashing via Flexible Semantic Calibration

Zheng Zhang, Luyao Liu, Yadan Luo, Zi Huang, and 3 more authors

IEEE Transactions on Neural Networks Learning Systems, 2021

DOI Bib PDF

@article{DBLP:journals/tnn/ZhangLLHSSL21,
  author = {Zhang, Zheng and Liu, Luyao and Luo, Yadan and Huang, Zi and Shen, Fumin and Shen, Heng Tao and Lu, Guangming},
  title = {Inductive Structure Consistent Hashing via Flexible Semantic Calibration},
  journal = {{IEEE} Transactions on Neural Networks Learning Systems},
  volume = {32},
  number = {10},
  pages = {4514--4528},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/TNNLS.2020.3018790},
  doi = {10.1109/TNNLS.2020.3018790},
  timestamp = {Wed, 16 Mar 2022 00:00:00 +0100},
  biburl = {https://dblp.org/rec/journals/tnn/ZhangLLHSSL21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://ieeexplore.ieee.org/document/9189794},
  preview = {isch.png},
  abbr = {TNNLS},
  topic = {adaptation}
}

WWW

High-order Nonlocal Hashing for Unsupervised Cross-modal Retrieval

Peng-Fei Zhang, Yadan Luo, Zi Huang, Xin-Shun Xu, and 1 more author

World Wide Web, 2021

DOI Bib PDF

@article{DBLP:journals/www/ZhangLHXS21,
  author = {Zhang, Peng{-}Fei and Luo, Yadan and Huang, Zi and Xu, Xin{-}Shun and Song, Jingkuan},
  title = {High-order Nonlocal Hashing for Unsupervised Cross-modal Retrieval},
  journal = {World Wide Web},
  volume = {24},
  number = {2},
  pages = {563--583},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1007/s11280-020-00859-y},
  doi = {10.1007/s11280-020-00859-y},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/journals/www/ZhangLHXS21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://link.springer.com/article/10.1007/s11280-020-00859-y},
  abbr = {WWW},
  topic = {adaptation}
}

IJCAI

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, and 1 more author

In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, 2021

@inproceedings{DBLP:conf/ijcai/LiCLYH21,
  author = {Li, Yang and Chen, Tong and Luo, Yadan and Yin, Hongzhi and Huang, Zi},
  editor = {Zhou, Zhi{-}Hua},
  title = {Discovering Collaborative Signals for Next {POI} Recommendation with
                 Iterative Seq2Graph Augmentation},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial
                 Intelligence, {IJCAI} 2021, Virtual Event / Montreal, Canada, 19-27
                 August 2021},
  pages = {1491--1497},
  publisher = {ijcai.org},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.24963/ijcai.2021/206},
  doi = {10.24963/ijcai.2021/206},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/ijcai/LiCLYH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://www.ijcai.org/proceedings/2021/0206.pdf},
  arxiv = {https://arxiv.org/abs/2106.15814},
  preview = {seq2graph.png},
  abbr = {IJCAI},
  topic = {adaptation}
}

Mitigating Generation Shifts for Generalized Zero-Shot Learning

Zhi Chen, Yadan Luo, Sen Wang, Ruihong Qiu, and 2 more authors

In MM ’21: ACM Multimedia Conference, Virtual Event, China, October 20 - 24, 2021, 2021

@inproceedings{DBLP:conf/mm/ChenL0QLH21,
  author = {Chen, Zhi and Luo, Yadan and Wang, Sen and Qiu, Ruihong and Li, Jingjing and Huang, Zi},
  editor = {Shen, Heng Tao and Zhuang, Yueting and Smith, John R. and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan},
  title = {Mitigating Generation Shifts for Generalized Zero-Shot Learning},
  booktitle = {{MM} '21: {ACM} Multimedia Conference, Virtual Event, China, October
                 20 - 24, 2021},
  pages = {844--852},
  publisher = {{ACM}},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3474085.3475258},
  doi = {10.1145/3474085.3475258},
  timestamp = {Thu, 13 Oct 2022 17:21:44 +0200},
  biburl = {https://dblp.org/rec/conf/mm/ChenL0QLH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  arxiv = {https://arxiv.org/abs/2107.03163},
  pdf = {https://dl.acm.org/doi/10.1145/3474085.3475258},
  preview = {gsmflow.png},
  abbr = {MM},
  topic = {adaptation}
}

MMAsia

Conditional Extreme Value Theory for Open Set Video Domain Adaptation

Zhuoxiao Chen, Yadan Luo, and Mahsa Baktashmotlagh

In MMAsia ’21: ACM Multimedia Asia, Gold Coast, Australia, December 1 - 3, 2021, 2021

@inproceedings{DBLP:conf/mmasia/ChenLB21,
  author = {Chen, Zhuoxiao and Luo, Yadan and Baktashmotlagh, Mahsa},
  editor = {Chen, Changwen and Huang, Helen and Zhou, Jun and Harada, Tatsuya and Cai, Jianfei and Liu, Wu and Xu, Dong},
  title = {Conditional Extreme Value Theory for Open Set Video Domain Adaptation},
  booktitle = {MMAsia '21: {ACM} Multimedia Asia, Gold Coast, Australia, December
                 1 - 3, 2021},
  pages = {20:1--20:8},
  publisher = {{ACM}},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3469877.3490600},
  doi = {10.1145/3469877.3490600},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mmasia/ChenLB21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/fullHtml/10.1145/3469877.3490600},
  preview = {cevt.jpg},
  code = {https://github.com/zhuoxiao-chen/CEVT},
  arxiv = {https://arxiv.org/abs/2109.00522},
  abbr = {MMAsia},
  topic = {adaptation}
}

MMAsia

RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management

Zhuoxiao Chen, Yiyun Zhang, Yadan Luo, Zijian Wang, and 2 more authors

In MMAsia ’21: ACM Multimedia Asia, Gold Coast, Australia, December 1 - 3, 2021, 2021

DOI Bib PDF Slides Website

@inproceedings{DBLP:conf/mmasia/ChenZLWZS21,
  author = {Chen, Zhuoxiao and Zhang, Yiyun and Luo, Yadan and Wang, Zijian and Zhong, Jinjiang and Southon, Anthony},
  editor = {Chen, Changwen and Huang, Helen and Zhou, Jun and Harada, Tatsuya and Cai, Jianfei and Liu, Wu and Xu, Dong},
  title = {RoadAtlas: Intelligent Platform for Automated Road Defect Detection
                 and Asset Management},
  booktitle = {MMAsia '21: {ACM} Multimedia Asia, Gold Coast, Australia, December
                 1 - 3, 2021},
  pages = {62:1--62:3},
  publisher = {{ACM}},
  year = {2021},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3469877.3493589},
  doi = {10.1145/3469877.3493589},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mmasia/ChenZLWZS21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/10.1145/3469877.3493589},
  slides = {https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3469877.3493589&file=RoadAtals_Demonstration_Slides.pptx},
  website = {https://lcc-atlas.uqcloud.net/},
  preview = {lcc.png},
  abbr = {MMAsia},
  topic = {autonomous-driving}
}

AAAI

Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks

Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, and 2 more authors

In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, 2020

arXiv Bib PDF Code Slides

@inproceedings{DBLP:conf/aaai/LuoHZWBY20,
  author = {Luo, Yadan and Huang, Zi and Zhang, Zheng and Wang, Ziwei and Baktashmotlagh, Mahsa and Yang, Yang},
  title = {Learning from the Past: Continual Meta-Learning with Bayesian Graph
                 Neural Networks},
  booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
                 2020, The Thirty-Second Innovative Applications of Artificial Intelligence
                 Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
                 Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
                 February 7-12, 2020},
  pages = {5021--5028},
  publisher = {{AAAI} Press},
  year = {2020},
  bibtex_show = {true},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5942},
  timestamp = {Thu, 30 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/aaai/LuoHZWBY20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://ojs.aaai.org/index.php/AAAI/article/view/5942/5798},
  arxiv = {https://arxiv.org/abs/1911.04695},
  code = {https://github.com/Luoyadan/BGNN-AAAI},
  slides = {https://www.youtube.com/watch?v=5Ao_Plrua3o},
  preview = {cml.png},
  abbr = {AAAI},
  topic = {adaptation}
}

ADC

PAIC: Parallelised Attentive Image Captioning

Ziwei Wang, Zi Huang, and Yadan Luo

In Databases Theory and Applications - 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings, 2020

🏆 Best Paper Award Awarded DOI Bib PDF

Best Paper Award

@inproceedings{DBLP:conf/adc/WangHL20,
  author = {Wang, Ziwei and Huang, Zi and Luo, Yadan},
  editor = {Borovica{-}Gajic, Renata and Qi, Jianzhong and Wang, Weiqing},
  title = {{PAIC:} Parallelised Attentive Image Captioning},
  booktitle = {Databases Theory and Applications - 31st Australasian Database Conference,
                 {ADC} 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings},
  series = {Lecture Notes in Computer Science},
  volume = {12008},
  pages = {16--28},
  publisher = {Springer},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1007/978-3-030-39469-1\_2},
  doi = {10.1007/978-3-030-39469-1\_2},
  timestamp = {Wed, 29 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/adc/WangHL20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://link.springer.com/chapter/10.1007/978-3-030-39469-1_2},
  award = {Best Paper Award},
  preview = {paic.png},
  abbr = {ADC},
  topic = {adaptation}
}

ADC

Semi-supervised Cross-Modal Hashing with Graph Convolutional Networks

Jiasheng Duan, Yadan Luo, Ziwei Wang, and Zi Huang

In Databases Theory and Applications - 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings, 2020

DOI Bib PDF

@inproceedings{DBLP:conf/adc/DuanLWH20,
  author = {Duan, Jiasheng and Luo, Yadan and Wang, Ziwei and Huang, Zi},
  editor = {Borovica{-}Gajic, Renata and Qi, Jianzhong and Wang, Weiqing},
  title = {Semi-supervised Cross-Modal Hashing with Graph Convolutional Networks},
  booktitle = {Databases Theory and Applications - 31st Australasian Database Conference,
                 {ADC} 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings},
  series = {Lecture Notes in Computer Science},
  volume = {12008},
  pages = {93--104},
  publisher = {Springer},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1007/978-3-030-39469-1\_8},
  doi = {10.1007/978-3-030-39469-1\_8},
  timestamp = {Wed, 29 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/adc/DuanLWH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://link.springer.com/chapter/10.1007/978-3-030-39469-1_8},
  abbr = {ADC},
  topic = {adaptation}
}

ADC

Graph-Based Relation-Aware Representation Learning for Clothing Matching

Yang Li, Yadan Luo, and Zi Huang

In Databases Theory and Applications - 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings, 2020

DOI Bib PDF

@inproceedings{DBLP:conf/adc/LiLH20,
  author = {Li, Yang and Luo, Yadan and Huang, Zi},
  editor = {Borovica{-}Gajic, Renata and Qi, Jianzhong and Wang, Weiqing},
  title = {Graph-Based Relation-Aware Representation Learning for Clothing Matching},
  booktitle = {Databases Theory and Applications - 31st Australasian Database Conference,
                 {ADC} 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings},
  series = {Lecture Notes in Computer Science},
  volume = {12008},
  pages = {189--197},
  publisher = {Springer},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1007/978-3-030-39469-1\_15},
  doi = {10.1007/978-3-030-39469-1\_15},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/adc/LiLH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://link.springer.com/chapter/10.1007/978-3-030-39469-1_15},
  abbr = {ADC},
  topic = {adaptation}
}

IJCAI

Human Consensus-Oriented Image Captioning

Ziwei Wang, Zi Huang, and Yadan Luo

In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, 2020

DOI Bib PDF

@inproceedings{DBLP:conf/ijcai/WangHL20,
  author = {Wang, Ziwei and Huang, Zi and Luo, Yadan},
  editor = {Bessiere, Christian},
  title = {Human Consensus-Oriented Image Captioning},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
                 Artificial Intelligence, {IJCAI} 2020},
  pages = {659--665},
  publisher = {ijcai.org},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.24963/ijcai.2020/92},
  doi = {10.24963/ijcai.2020/92},
  timestamp = {Wed, 29 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/ijcai/WangHL20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://www.ijcai.org/proceedings/2020/0092.pdf},
  preview = {hco.png},
  abbr = {IJCAI},
  topic = {adaptation}
}

Adversarial Bipartite Graph Learning for Video Domain Adaptation

Yadan Luo, Zi Huang, Zijian Wang, Zheng Zhang, and 1 more author

In MM ’20: The 28th ACM International Conference on Multimedia, 2020

DOI Bib Code Slides

@inproceedings{DBLP:conf/mm/LuoHW0B20,
  author = {Luo, Yadan and Huang, Zi and Wang, Zijian and Zhang, Zheng and Baktashmotlagh, Mahsa},
  editor = {Chen, Chang Wen and Cucchiara, Rita and Hua, Xian{-}Sheng and Qi, Guo{-}Jun and Ricci, Elisa and Zhang, Zhengyou and Zimmermann, Roger},
  title = {Adversarial Bipartite Graph Learning for Video Domain Adaptation},
  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia},
  pages = {19--27},
  publisher = {{ACM}},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3394171.3413897},
  doi = {10.1145/3394171.3413897},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mm/LuoHW0B20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  code = {https://github.com/Luoyadan/MM2020_ABG},
  slides = {https://www.youtube.com/watch?v=K8geLQijHqY},
  preview = {ABG.png},
  abbr = {MM},
  topic = {adaptation}
}

Prototype-Matching Graph Network for Heterogeneous Domain Adaptation

Zijian Wang, Yadan Luo, Zi Huang, and Mahsa Baktashmotlagh

In MM ’20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12-16, 2020, 2020

@inproceedings{DBLP:conf/mm/WangLHB20,
  author = {Wang, Zijian and Luo, Yadan and Huang, Zi and Baktashmotlagh, Mahsa},
  editor = {Chen, Chang Wen and Cucchiara, Rita and Hua, Xian{-}Sheng and Qi, Guo{-}Jun and Ricci, Elisa and Zhang, Zhengyou and Zimmermann, Roger},
  title = {Prototype-Matching Graph Network for Heterogeneous Domain Adaptation},
  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia, Virtual
                 Event / Seattle, WA, USA, October 12-16, 2020},
  pages = {2104--2112},
  publisher = {{ACM}},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3394171.3413662},
  doi = {10.1145/3394171.3413662},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mm/WangLHB20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/10.1145/3394171.3413662},
  code = {https://github.com/BUserName/PMGN},
  preview = {pmgn.png},
  topic = {adaptation}
}

PAKDD

Fashion Recommendation with Multi-relational Representation Learning

Yang Li, Yadan Luo, and Zi Huang

In Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings, Part I, 2020

DOI Bib PDF

@inproceedings{DBLP:conf/pakdd/LiLH20,
  author = {Li, Yang and Luo, Yadan and Huang, Zi},
  editor = {Lauw, Hady W. and Wong, Raymond Chi{-}Wing and Ntoulas, Alexandros and Lim, Ee{-}Peng and Ng, See{-}Kiong and Pan, Sinno Jialin},
  title = {Fashion Recommendation with Multi-relational Representation Learning},
  booktitle = {Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia
                 Conference, {PAKDD} 2020, Singapore, May 11-14, 2020, Proceedings,
                 Part {I}},
  series = {Lecture Notes in Computer Science},
  volume = {12084},
  pages = {3--15},
  publisher = {Springer},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1007/978-3-030-47426-3\_1},
  doi = {10.1007/978-3-030-47426-3\_1},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/pakdd/LiLH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://link.springer.com/chapter/10.1007/978-3-030-47426-3_1},
  abbr = {PAKDD},
  topic = {adaptation}
}

WACV

CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language

Zhi Chen, Jingjing Li, Yadan Luo, Zi Huang, and 1 more author

In IEEE Winter Conference on Applications of Computer Vision, WACV 2020, Snowmass Village, CO, USA, March 1-5, 2020, 2020

@inproceedings{DBLP:conf/wacv/ChenLLHY20,
  author = {Chen, Zhi and Li, Jingjing and Luo, Yadan and Huang, Zi and Yangyang, Yangyang},
  title = {{CANZSL:} Cycle-Consistent Adversarial Networks for Zero-Shot Learning
                 from Natural Language},
  booktitle = {{IEEE} Winter Conference on Applications of Computer Vision, {WACV}
                 2020, Snowmass Village, CO, USA, March 1-5, 2020},
  pages = {863--872},
  publisher = {{IEEE}},
  year = {2020},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/WACV45572.2020.9093610},
  doi = {10.1109/WACV45572.2020.9093610},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/wacv/ChenLLHY20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  abbr = {WACV},
  arxiv = {https://arxiv.org/abs/1909.09822},
  pdf = {https://ieeexplore.ieee.org/iel7/9087828/9093261/09093610.pdf},
  topic = {adaptation}
}

Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation

Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, and 2 more authors

In Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, October 21-25, 2019, 2019

@inproceedings{DBLP:conf/mm/LuoHZWL019,
  author = {Luo, Yadan and Huang, Zi and Zhang, Zheng and Wang, Ziwei and Li, Jingjing and Yang, Yang},
  editor = {Amsaleg, Laurent and Huet, Benoit and Larson, Martha A. and Gravier, Guillaume and Hung, Hayley and Ngo, Chong{-}Wah and Ooi, Wei Tsang},
  title = {Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph
                 Generation},
  booktitle = {Proceedings of the 27th {ACM} International Conference on Multimedia,
                 {MM} 2019, Nice, France, October 21-25, 2019},
  pages = {2341--2350},
  publisher = {{ACM}},
  year = {2019},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3343031.3350961},
  doi = {10.1145/3343031.3350961},
  timestamp = {Wed, 29 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mm/LuoHZWL019.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  arxiv = {https://arxiv.org/abs/1908.00169},
  pdf = {https://dl.acm.org/doi/10.1145/3343031.3350961},
  preview = {curiosity.png},
  abbr = {MM},
  topic = {adaptation}
}

Collaborative Generative Adversarial Network for Recommendation Systems

Yuzhen Tong, Yadan Luo, Zheng Zhang, Shazia W. Sadiq, and 1 more author

In 35th IEEE International Conference on Data Engineering Workshops, ICDE Workshops 2019, Macao, China, April 8-12, 2019, 2019

DOI Bib PDF

@inproceedings{DBLP:conf/icde/TongLZSC19,
  author = {Tong, Yuzhen and Luo, Yadan and Zhang, Zheng and Sadiq, Shazia W. and Cui, Peng},
  title = {Collaborative Generative Adversarial Network for Recommendation Systems},
  booktitle = {35th {IEEE} International Conference on Data Engineering Workshops,
                 {ICDE} Workshops 2019, Macao, China, April 8-12, 2019},
  pages = {161--168},
  publisher = {{IEEE}},
  year = {2019},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/ICDEW.2019.00-16},
  doi = {10.1109/ICDEW.2019.00-16},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/icde/TongLZSC19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://ieeexplore.ieee.org/document/8750926},
  topic = {adaptation}
}

ICDE

Context-Aware Attention-Based Data Augmentation for POI Recommendation

Yang Li, Yadan Luo, Zheng Zhang, Shazia W. Sadiq, and 1 more author

In 35th IEEE International Conference on Data Engineering Workshops, ICDE Workshops 2019, Macao, China, April 8-12, 2019, 2019

@inproceedings{DBLP:conf/icde/LiLZSC19,
  author = {Li, Yang and Luo, Yadan and Zhang, Zheng and Sadiq, Shazia W. and Cui, Peng},
  title = {Context-Aware Attention-Based Data Augmentation for {POI} Recommendation},
  booktitle = {35th {IEEE} International Conference on Data Engineering Workshops,
                 {ICDE} Workshops 2019, Macao, China, April 8-12, 2019},
  pages = {177--184},
  publisher = {{IEEE}},
  year = {2019},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/ICDEW.2019.00-14},
  doi = {10.1109/ICDEW.2019.00-14},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/icde/LiLZSC19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://ieeexplore.ieee.org/document/8750927},
  preview = {poi.gif},
  arxiv = {https://arxiv.org/pdf/2106.15984},
  abbr = {ICDE},
  topic = {adaptation}
}

ICME

Cycle-Consistent Diverse Image Synthesis from Natural Language

Zhi Chen and Yadan Luo

In IEEE International Conference on Multimedia & Expo Workshops, ICME Workshops 2019, Shanghai, China, July 8-12, 2019, 2019

DOI Bib PDF

@inproceedings{DBLP:conf/icmcs/ChenL19,
  author = {Chen, Zhi and Luo, Yadan},
  title = {Cycle-Consistent Diverse Image Synthesis from Natural Language},
  booktitle = {{IEEE} International Conference on Multimedia {\&} Expo Workshops,
                 {ICME} Workshops 2019, Shanghai, China, July 8-12, 2019},
  pages = {459--464},
  publisher = {{IEEE}},
  year = {2019},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/ICMEW.2019.00085},
  doi = {10.1109/ICMEW.2019.00085},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/icmcs/ChenL19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://ieeexplore.ieee.org/iel7/8777006/8794861/08795022.pdf},
  abbr = {ICME},
  topic = {adaptation}
}

SIGIR

Deep Collaborative Discrete Hashing with Semantic-Invariant Structure

Zijian Wang, Zheng Zhang, Yadan Luo, and Zi Huang

In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, 2019

DOI Bib PDF

@inproceedings{DBLP:conf/sigir/WangZLH19,
  author = {Wang, Zijian and Zhang, Zheng and Luo, Yadan and Huang, Zi},
  editor = {Piwowarski, Benjamin and Chevalier, Max and {\'{E}}ric Gaussier and Maarek, Yoelle and Nie, Jian{-}Yun and Scholer, Falk},
  title = {Deep Collaborative Discrete Hashing with Semantic-Invariant Structure},
  booktitle = {Proceedings of the 42nd International {ACM} {SIGIR} Conference on
                 Research and Development in Information Retrieval, {SIGIR} 2019, Paris,
                 France, July 21-25, 2019},
  pages = {905--908},
  publisher = {{ACM}},
  year = {2019},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3331184.3331275},
  doi = {10.1145/3331184.3331275},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/sigir/WangZLH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/10.1145/3331184.3331275},
  abbr = {SIGIR},
  topic = {adaptation}
}

Robust Discrete Code Modeling for Supervised Hashing

Yadan Luo, Yang Yang, Fumin Shen, Zi Huang, and 2 more authors

Pattern Recognition, 2018

DOI Bib PDF

@article{DBLP:journals/pr/LuoYSHZS18,
  author = {Luo, Yadan and Yang, Yang and Shen, Fumin and Huang, Zi and Zhou, Pan and Shen, Heng Tao},
  title = {Robust Discrete Code Modeling for Supervised Hashing},
  journal = {Pattern Recognition},
  volume = {75},
  pages = {128--135},
  year = {2018},
  bibtex_show = {true},
  url = {https://doi.org/10.1016/j.patcog.2017.02.034},
  doi = {10.1016/j.patcog.2017.02.034},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/journals/pr/LuoYSHZS18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://www.sciencedirect.com/science/article/pii/S0031320317300973},
  preview = {dsh.jpg},
  abbr = {PR},
  topic = {adaptation}
}

CIKM

Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Yadan Luo, Ziwei Wang, Zi Huang, Yang Yang, and 1 more author

In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, 2018

@inproceedings{DBLP:conf/cikm/LuoWHYZ18,
  author = {Luo, Yadan and Wang, Ziwei and Huang, Zi and Yang, Yang and Zhao, Cong},
  editor = {Cuzzocrea, Alfredo and Allan, James and Paton, Norman W. and Srivastava, Divesh and Agrawal, Rakesh and Broder, Andrei Z. and Zaki, Mohammed J. and Candan, K. Sel{\c{c}}uk and Labrinidis, Alexandros and Schuster, Assaf and Wang, Haixun},
  title = {Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning},
  booktitle = {Proceedings of the 27th {ACM} International Conference on Information
                 and Knowledge Management, {CIKM} 2018, Torino, Italy, October 22-26,
                 2018},
  pages = {237--246},
  publisher = {{ACM}},
  year = {2018},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3269206.3271672},
  doi = {10.1145/3269206.3271672},
  timestamp = {Wed, 29 Jun 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/cikm/LuoWHYZ18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  arxiv = {https://arxiv.org/abs/1808.07209},
  pdf = {https://doi.org/10.1145/3269206.3271672},
  preview = {coarse.png},
  abbr = {CIKM},
  topic = {adaptation}
}

Look Deeper See Richer: Depth-aware Image Paragraph Captioning

Ziwei Wang*, Yadan Luo*, Yang Li, Zi Huang, and 1 more author

In 2018 ACM Multimedia Conference on Multimedia Conference, MM 2018, Seoul, Republic of Korea, October 22-26, 2018, 2018

DOI Bib PDF

@inproceedings{DBLP:conf/mm/WangLLHY18,
  author = {Wang*, Ziwei and Luo*, Yadan and Li, Yang and Huang, Zi and Yin, Hongzhi},
  editor = {Boll, Susanne and Lee, Kyoung Mu and Luo, Jiebo and Zhu, Wenwu and Byun, Hyeran and Chen, Chang Wen and Lienhart, Rainer and Mei, Tao},
  title = {Look Deeper See Richer: Depth-aware Image Paragraph Captioning},
  booktitle = {2018 {ACM} Multimedia Conference on Multimedia Conference, {MM} 2018,
                 Seoul, Republic of Korea, October 22-26, 2018},
  pages = {672--680},
  publisher = {{ACM}},
  year = {2018},
  bibtex_show = {true},
  url = {https://doi.org/10.1145/3240508.3240583},
  doi = {10.1145/3240508.3240583},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/mm/WangLLHY18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://dl.acm.org/doi/10.1145/3240508.3240583},
  preview = {dam.png},
  abbr = {MM},
  topic = {adaptation}
}

Deep Reinforcement Learning-based Vehicle Energy Efficiency Autonomous Learning System

Xuewei Qi, Yadan Luo, Guoyuan Wu, Kanok Boriboonsomsin, and 1 more author

In IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017, 2017

@inproceedings{DBLP:conf/ivs/QiLWBB17,
  author = {Qi, Xuewei and Luo, Yadan and Wu, Guoyuan and Boriboonsomsin, Kanok and Barth, Matthew J.},
  title = {Deep Reinforcement Learning-based Vehicle Energy Efficiency Autonomous
                 Learning System},
  booktitle = {{IEEE} Intelligent Vehicles Symposium, {IV} 2017, Los Angeles, CA,
                 USA, June 11-14, 2017},
  pages = {1228--1233},
  publisher = {{IEEE}},
  year = {2017},
  bibtex_show = {true},
  url = {https://doi.org/10.1109/IVS.2017.7995880},
  doi = {10.1109/IVS.2017.7995880},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl = {https://dblp.org/rec/conf/ivs/QiLWBB17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  pdf = {https://ieeexplore.ieee.org/document/7995880},
  code = {https://github.com/Luoyadan/DQN_vehicle_energy},
  preview = {rl.png},
  abbr = {IV},
  topic = {autonomous-driving}
}

Zero-Shot Hashing via Transferring Supervised Knowledge

Yang Yang, Yadan Luo, Weilun Chen, Fumin Shen, and 2 more authors

In Proceedings of the 2016 ACM Conference on Multimedia Conference, MM 2016, Amsterdam, The Netherlands, October 15-19, 2016, 2016