Joint Sample Enhancement and Instance-Sensitive Feature Learning for Efficient Person Search

Ke, Xiao; Liu, Hao; Guo, Wenzhong; Chen, Baitao; Cai, Yuhang; Chen, Weibin

doi:10.1109/tcsvt.2022.3188551

Cited by 10 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Person search is a joint task of pedestrian detection and person re-id, which plays an important role in video surveillance [15], [16], [3], [4]. Existing person search methods can be divided into two main categories: two-step methods and one-step methods.…”

Section: A Person Searchmentioning

confidence: 99%

Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search

Wang¹,

Pang²,

Cao³

et al. 2023

Preprint

View full text Add to dashboard Cite

Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only person bounding-box annotations. Recently, the idea of contrastive learning is initially applied to weakly supervised person search, where two common contrast strategies are memorybased contrast and intra-image contrast. We argue that current intra-image contrast is shallow, which suffers from spatiallevel and occlusion-level variance. In this paper, we present a novel deep intra-image contrastive learning using a Siamese network. Two key modules are spatial-invariant contrast (SIC) and occlusion-invariant contrast (OIC). SIC performs many-toone contrasts between two branches of Siamese network and dense prediction contrasts in one branch of Siamese network. With these many-to-one and dense contrasts, SIC tends to learn discriminative scale-invariant and location-invariant features to solve spatial-level variance. OIC enhances feature consistency with the masking strategy to learn occlusion-invariant features. Extensive experiments are performed on two person search datasets CUHK-SYSU and PRW, respectively. Our method achieves a state-of-the-art performance among weakly supervised one-step person search approaches. We hope that our simple intra-image contrastive learning can provide more paradigms on weakly supervised person search. The source code is available at https://github.com/jiabeiwangTJU/DICL.

show abstract