no code implementations • 27 Sep 2022 • Chengzhi Lin, AnCong Wu, Junwei Liang, Jun Zhang, Wenhang Ge, Wei-Shi Zheng, Chunhua Shen
To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching model, which automatically captures multiple prototypes to describe a video by adaptive aggregation of video token features.
1 code implementation • CVPR 2022 • Chao Wu, Wenhang Ge, AnCong Wu, Xiaobin Chang
To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role.
no code implementations • 6 Dec 2021 • Zelin Chen, Hong-Xing Yu, AnCong Wu, Wei-Shi Zheng
To make the application of writer-id more practical (e. g., on mobile devices), we focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues.
1 code implementation • 29 Jul 2021 • Wenhang Ge, Chunyan Pan, AnCong Wu, Hongwei Zheng, Wei-Shi Zheng
To learn camera-invariant representation from cross-camera unpaired training data, we propose a cross-camera feature prediction method to mine cross-camera self supervision information from camera-specific feature distribution by transforming fake cross-camera positive feature pairs and minimize the distances of the fake pairs.
no code implementations • CVPR 2021 • Peixian Hong, Tao Wu, AnCong Wu, Xintong Han, Wei-Shi Zheng
Recently, person re-identification (Re-ID) has achieved great progress.
Ranked #3 on Person Re-Identification on PRCC
1 code implementation • 10 Dec 2020 • Enwei Zhang, Xinyang Jiang, Hao Cheng, AnCong Wu, Fufu Yu, Ke Li, Xiaowei Guo, Feng Zheng, Wei-Shi Zheng, Xing Sun
Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch.
no code implementations • ICCV 2019 • Ancong Wu, Wei-Shi Zheng, Jian-Huang Lai
To alleviate the effect of cross-camera scene variation, we propose a Camera-Aware Similarity Consistency Loss to learn consistent pairwise similarity distributions for intra-camera matching and cross-camera matching.
no code implementations • CVPR 2019 • Ancong Wu, Wei-Shi Zheng, Xiaowei Guo, Jian-Huang Lai
To solve these problems in a unified system, we propose a Multi-teacher Adaptive Similarity Distillation Framework, which requires only a few labelled identities of target domain to transfer knowledge from multiple teacher models to a user-specified lightweight student model without accessing source domain data.
no code implementations • ICCV 2017 • Ancong Wu, Wei-Shi Zheng, Hong-Xing Yu, Shaogang Gong, Jian-Huang Lai
To that end, matching RGB images with infrared images is required, which are heterogeneous with very different visual characteristics.
Ranked #3 on Cross-Modal Person Re-Identification on SYSU-MM01 (mAP (All-search & Single-shot) metric)
Cross-Modality Person Re-identification Cross-Modal Person Re-Identification
no code implementations • 28 Mar 2017 • Ancong Wu, Wei-Shi Zheng, Jian-Huang Lai
More specifically, we exploit depth voxel covariance descriptor and further propose a locally rotation invariant depth shape descriptor called Eigen-depth feature to describe pedestrian body shape.