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Preprints

  • Zhenfang Chen, Lin Ma, Wenhan Luo, Peng Tang, Kwan-Yee K Wong. “Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video”. arxiv, 2020. (arxiv)

Conference papers

  • Peng Tang, Pengkai Zhu, Tian Li, Srikar Appalaraju, Vijay Mahadevan, R. Manmatha. “DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models”. The North American Chapter of the Association for Computational Linguistics (NAACL) Findings, 2024. (arxiv)
  • Peng Tang, Srikar Appalaraju, R. Manmatha, Yusheng Xie, Vijay Mahadevan. “Multiple-Question Multiple-Answer Text-VQA”. The North American Chapter of the Association for Computational Linguistics (NAACL) Industry Track, 2024. (arxiv)
  • Zhuowan Li, Bhavan Jasani, Peng Tang, Shabnam Ghadar. “Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (arxiv)
  • Srikar Appalaraju, Peng Tang, Qi Dong, Nishant Sankaran, Yichu Zhou, R. Manmatha. “DocFormerv2: Local Features for Document Understanding”. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024. (arxiv)
  • Tianyang Zhao, Kunwar Yashraj Singh, Srikar Appalaraju, Peng Tang, Vijay Mahadevan, R. Manmatha, Ying Nian Wu. “No Head Left Behind - Multi-Head Alignment Distillation for Transformers”. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024. (PDF)
  • Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie. “Shape-Texture Debiased Neural Network Training”. International Conference on Learning Representations (ICLR), 2021. (CODE) (arxiv)
  • Peng Tang, Chetan Ramaiah, Yan Wang, Ran Xu, Caiming Xiong. “Proposal Learning for Semi-Supervised Object Detection”. IEEE Winter Conference on Applications of Computer Vision (WACV), 2021. (arxiv)
  • Hongru Zhu, Peng Tang, Jeongho Park, Soojin Park, Alan Yuille. “Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models”. Annual Meeting of the Cognitive Science Society (CogSci), 2019. (arxiv)
  • Song Bai, Peng Tang, Philip Torr, Longin Jan Latecki. “Re-ranking via Metric Fusion for Object Retrieval and Person Re-identification”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (PDF)
  • Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan Yuille. “Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training”. IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. (PDF) (arxiv)
  • Peng Tang, Xinggang Wang, Angtian Wang, Yongluan Yan, Wenyu Liu, Junzhou Huang, Alan Yuille. “Weakly Supervised Region Proposal Network and Object Detection”. European Conference on Computer Vision (ECCV), 2018. (PDF) (supp)
  • Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan Yuille. “Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound”. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), oral, 2018. (arxiv)
  • Gangming Zhao, Zhaoxiang Zhang, He Guan, Peng Tang, Jingdong Wang. “Rethinking ReLU to Train Better CNNs”. International Conference on Pattern Recognition (ICPR), 2018. (PDF)
  • Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu. “Multiple Instance Detection Network with Online Instance Classifier Refinement”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (PDF) (CODE) (arxiv)

Journal papers

  • Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan Yuille. “Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction”. IEEE Transactions on Medical Imaging, 2021. (PDF)
  • Peng Tang, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wenjun Zeng, Jingdong Wang. “Object Detection in Videos by High Quality Object Linking”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. (PDF) (arxiv)
  • Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille. “PCL: Proposal Cluster Learning for Weakly Supervised Object Detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. (PDF) (PyTorch CODE) (Caffe CODE) (arxiv)
  • Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen Tu. “Deep FisherNet for Image Classification”. IEEE Transactions on Neural Networks and Learning Systems, 2018. (PDF) (arxiv)
  • Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, Wenyu Liu. “Revisiting Multiple Instance Neural Networks”. Pattern Recognition, 2018. (PDF) (CODE) (arxiv)
  • Peng Tang, Xinggang Wang, Zilong Huang, Xiang Bai, Wenyu Liu. “Deep Patch Learning for Weakly Supervised Object Classification and Discovery”. Pattern Recognition, 2017. (PDF) (CODE) (arxiv)
  • Peng Tang, Xinggang Wang, Bin Feng, Wenyu Liu. “Learning Multi-instance Deep Discriminative Patterns for Image Classification”. IEEE Transactions on Image Processing, 2017. (PDF)
  • Peng Tang, Jin Zhang, Xinggang Wang, Bin Feng, Fabio Roli, Wenyu Liu. “Learning Extremely Shared Middle-level Image Representation for Scene Classification”. Knowledge and Information Systems, 2017. (PDF) (CODE)