publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2024

  1. dycoke-demo.gif
    DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models
    Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang
    arXiv preprint arXiv:2411.15024, 2024
    arXiv Code
  2. xgen-mm-vid1.gif
    xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs
    Michael S Ryoo, Honglu Zhou, Shrikant Kendre, Can Qin, Le Xue, Manli Shu, Silvio Savarese, Ran Xu, Caiming Xiong, and Juan Carlos Niebles
    arXiv preprint arXiv:2410.16267, 2024
    arXiv Website
  3. xgen-videosyn-1.gif
    xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations
    Can Qin, Congying Xia, Krithika Ramakrishnan, Michael Ryoo, Lifu Tu, Yihao Feng, Manli Shu, Honglu Zhou, Anas Awadalla, Jun Wang, and  others
    arXiv preprint arXiv:2408.12590, 2024
    arXiv Code
  4. blip3.png
    xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
    Le Xue, Manli Shu, Anas Awadalla, Jun Wang, An Yan, Senthil Purushwalkam, Honglu Zhou, Viraj Prabhu, Yutong Dai, Michael S Ryoo, and  others
    arXiv preprint arXiv:2408.08872, 2024
    arXiv Code
  5. preference_data_st_llava_med.png
    Self-Training Large Language and Vision Assistant for Medical
    Guohao Sun, Can Qin, Huazhu Fu, Linwei Wang, and Zhiqiang Tao
    In The 2024 Conference on Empirical Methods in Natural Language Processing, 2024
    arXiv Code
  6. sq-llava.png
    SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant
    Guohao Sun, Can Qin, Jiamian Wang, Zeyuan Chen, Ran Xu, and Zhiqiang Tao
    European Conference on Computer Vision, 2024
    arXiv Code
  7. hive.png
    HIVE: Harnessing Human Feedback for Instructional Visual Editing
    Shu Zhang*, Xinyi Yang*, Yihao Feng*, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, and Ran Xu
    IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
    arXiv Code

2023

  1. arXiv
    Why is the state of neural network pruning so confusing? on the fairness, comparison setup, and trainability in network pruning
    Huan Wang, Can Qin, Yue Bai, and Yun Fu
    arXiv:2301.05219, 2023
    arXiv Code
  2. unicontrol.png
    UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
    Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, Stefano Ermon, Yun Fu, and Ran Xu
    Advances in Neural Information Processing Systems, 2023
    arXiv Code Website
  3. ICDM
    Rethinking Adam: A twofold exponential moving average approach
    Yizhou Wang, Yue Kang, Can Qin, Huan Wang, Yi Xu, Yulun Zhang, and Yun Fu
    IEEE International Conference on Data Mining, 2023
    arXiv Code
  4. gluegen.png
    GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation
    Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, and Ran Xu
    International Conference on Computer Vision, 2023
    arXiv Code Website
  5. TIP
    Balancing biases and preserving privacy on balanced faces in the wild
    Joseph P Robinson, Can Qin, Yann Henon, Samson Timoner, and Yun Fu
    IEEE Transactions on Image Processing, 2023
    arXiv Code
  6. TPAMI
    Global Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
    Huan Wang, Yulun Zhang, Can Qin, Luc Van Gool, and Yun Fu
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
    PDF Code
  7. CVPR
    Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
    Vibashan VS, Ning Yu, Chen Xing, Can Qin, Mingfei Gao, Juan Carlos Niebles, Vishal M Patel, and Ran Xu
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
    PDF Code
  8. ICLR Oral
    Image as Set of Points
    Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, and Yun Fu
    In The Eleventh International Conference on Learning Representations, 2023
    arXiv Code

2022

  1. arXiv
    A Close Look at Spatial Modeling: From Attention to Convolution
    Xu Ma, Huan Wang, Can Qin, Kunpeng Li, Xingchen Zhao, Jie Fu, and Yun Fu
    arXiv preprint arXiv:2212.12552, 2022
    arXiv Code
  2. ICDM
    Making Reconstruction-based Method Great Again for Video Anomaly Detection
    Yizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma, and Yun Fu
    In 2022 IEEE International Conference on Data Mining, 2022
    arXiv Code
  3. TIP
    Semi-Supervised Domain Adaptive Structure Learning
    Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, and Yun Fu
    IEEE Transactions on Image Processing, 2022
    arXiv Code
  4. KDD
    External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters
    Can Qin, Sungchul Kim, Handong Zhao, Tong Yu, Ryan A Rossi, and Yun Fu
    In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
    PDF Code
  5. ICLR
    Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
    Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu
    In International Conference on Learning Representations, 2022
    Code
  6. ICLR
    Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning
    Yulun Zhang*, Huan Wang*, Can Qin, and Yun Fu
    In International Conference on Learning Representations, 2022
    PDF Code
  7. Nature Comm
    Self-directed online machine learning for topology optimization
    Changyu Deng, Yizhou Wang, Can Qin, Yun Fu, and Wei Lu
    Nature communications, 2022
    Code

2021

  1. FG
    The 5th recognizing families in the wild data challenge: Predicting kinship from faces
    Joseph P Robinson, Can Qin, Ming Shao, Matthew A Turk, Rama Chellappa, and Yun Fu
    In IEEE International Conference on Automatic Face and Gesture Recognition, 2021
    arXiv
  2. NeurIPS
    Slow learning and fast inference: Efficient graph similarity computation via knowledge distillation
    Can Qin, Handong Zhao, Lichen Wang, Huan Wang, Yulun Zhang, and Yun Fu
    Advances in Neural Information Processing Systems, 2021
    PDF Code
  3. NeurIPS Spotlight
    Aligned structured sparsity learning for efficient image super-resolution
    Yulun Zhang*, Huan Wang*, Can Qin, and Yun Fu
    Advances in Neural Information Processing Systems, 2021
    PDF Code
  4. ICCV
    Context reasoning attention network for image super-resolution
    Yulun Zhang, Donglai Wei, Can Qin, Huan Wang, Hanspeter Pfister, and Yun Fu
    In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021
    PDF
  5. SDM
    Contradictory structure learning for semi-supervised domain adaptation
    Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, and Yun Fu
    In SIAM International Conference on Data Mining, 2021
    arXiv Code
  6. ICLR
    Neural pruning via growing regularization
    Huan Wang, Can Qin, Yulun Zhang, and Yun Fu
    International Conference on Learning Representations, 2021
    arXiv Code

2020

  1. CVPRW
    Face recognition: too bias, or not too bias?
    Joseph P Robinson, Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020
    PDF Code
  2. ECCV
    Generative view-correlation adaptation for semi-supervised multi-view learning
    Yunyu Liu, Lichen Wang, Yue Bai, Can Qin, Zhengming Ding, and Yun Fu
    In European Conference on Computer Vision, 2020
    PDF Code
  3. AAAI
    Dual relation semi-supervised multi-label learning
    Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, and Yun Fu
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2020
    PDF
  4. Remote Sensing
    Semi-supervised hyperspectral image classification via spatial-regulated self-training
    Yue Wu, Guifeng Mu, Can Qin, Qiguang Miao, Wenping Ma, and Xiangrong Zhang
    Remote Sensing, 2020
    PDF

2019

  1. NeurIPS
    Pointdan: A multi-scale 3d domain adaption network for point cloud representation
    Can Qin*, Haoxuan You*, Lichen Wang, C-C Jay Kuo, and Yun Fu
    Advances in Neural Information Processing Systems, 2019
    PDF Code
  2. ICCVW
    Generatively inferential co-training for unsupervised domain adaptation
    Can Qin, Lichen Wang, Yulun Zhang, and Yun Fu
    In IEEE/CVF International Conference on Computer Vision Workshops, 2019
    PDF Code