activity
20162021
most citedAn Enhanced Deep Feature Representation for Person Re-identification

198 citations · 252 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2021★ 4 cited

Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding

Chaolei Tan, Zihang Lin, Jian-Fang Hu +2

We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In the first stage, we propose an A…

cs.CV2019★ 16 cited

Cross-view Relation Networks for Mammogram Mass Detection

Jiechao Ma, Sen Liang, Xiang Li +4

Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage. The information from the two paired views (i.e., medio-lateral o…

cs.CV2019★ 4 cited

Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks

Xiang Li, Chan Lu, Danni Cheng +5

Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential. To evaluate and improve the…

cs.CV2018★ 3 cited

Adversarial Open-World Person Re-Identification

Xiang Li, Ancong Wu, Wei-Shi Zheng

In a typical real-world application of re-id, a watch-list (gallery set) of a handful of target people (e.g. suspects) to track around a large volume of non-target people are deman…

cs.CV2016★ 198 cited

An Enhanced Deep Feature Representation for Person Re-identification

Shangxuan Wu, Ying-Cong Chen, Xiang Li +3

Feature representation and metric learning are two critical components in person re-identification models. In this paper, we focus on the feature representation and claim that hand…

cs.CV2016★ 27 cited

Top-push Video-based Person Re-identification

Jinjie You, Ancong Wu, Xiang Li +1

Most existing person re-identification (re-id) models focus on matching still person images across disjoint camera views. Since only limited information can be exploited from still…