1 code implementation • 19 Jan 2023 • Huafeng Liu, Pai Peng, Tao Chen, Qiong Wang, Yazhou Yao, Xian-Sheng Hua
Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images.
Ranked #2 on Few-Shot Semantic Segmentation on COCO-20i (10-shot)
no code implementations • 5 Oct 2022 • Tianwen Qian, Ran Cui, Jingjing Chen, Pai Peng, Xiaowei Guo, Yu-Gang Jiang
Considering the fact that the question often remains concentrated in a short temporal range, we propose to first locate the question to a segment in the video and then infer the answer using the located segment only.
no code implementations • 20 Sep 2022 • Yang Wu, Pai Peng, Zhenyu Zhang, Yanyan Zhao, Bing Qin
At the low-level, we propose the progressive tri-modal attention, which can model the tri-modal feature interactions by adopting a two-pass strategy and can further leverage such interactions to significantly reduce the computation and memory complexity through reducing the input token length.
1 code implementation • 26 Apr 2022 • Jiale Wei, Qiyuan Chen, Pai Peng, Benjamin Guedj, Le Li
This paper presents REPRINT, a simple and effective hidden-space data augmentation method for imbalanced data classification.
1 code implementation • 20 Apr 2022 • Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun, Yu-Gang Jiang
Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor.
no code implementations • ICCV 2021 • Runnan Chen, Penghao Zhou, Wenzhe Wang, Nenglun Chen, Pai Peng, Xing Sun, Wenping Wang
Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention.
no code implementations • CVPR 2021 • Shang-Hua Gao, Qi Han, Duo Li, Ming-Ming Cheng, Pai Peng
We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost.
1 code implementation • ICCV 2021 • Guanyu Cai, Jun Zhang, Xinyang Jiang, Yifei Gong, Lianghua He, Fufu Yu, Pai Peng, Xiaowei Guo, Feiyue Huang, Xing Sun
However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description.
2 code implementations • 8 Jan 2021 • Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, Xing Sun
Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales.
Ranked #8 on Text based Person Retrieval on CUHK-PEDES
2 code implementations • CVPR 2021 • Shang-Hua Gao, Qi Han, Zhong-Yu Li, Pai Peng, Liang Wang, Ming-Ming Cheng
Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further.
Ranked #14 on Action Segmentation on Breakfast
2 code implementations • CVPR 2021 • Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J. Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, Xing Sun
Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes.
1 code implementation • 27 Jul 2020 • Penghao Zhou, Chong Zhou, Pai Peng, Junlong Du, Xing Sun, Xiaowei Guo, Feiyue Huang
Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives.
Ranked #7 on Object Detection on CrowdHuman (full body)
no code implementations • 28 Jul 2017 • Ziliang Chen, Keze Wang, Xiao Wang, Pai Peng, Ebroul Izquierdo, Liang Lin
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS).