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20182023
most citedHetero-Center Loss for Cross-Modality Person Re-Identification

10 citations · 32 across the 8 of their papers we have counts for

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10 papers · 1 filter

cs.CV2023

Progressive Feature Mining and External Knowledge-Assisted Text-Pedestrian Image Retrieval

Huafeng Li, Shedan Yang, Yafei Zhang +2

Text-Pedestrian Image Retrieval aims to use the text describing pedestrian appearance to retrieve the corresponding pedestrian image. This task involves not only modality discrepan…

cs.CV20231 cited

Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification

Huafeng Li, Le Xu, Yafei Zhang +2

In visible-infrared video person re-identification (re-ID), extracting features not affected by complex scenes (such as modality, camera views, pedestrian pose, background, etc.) c…

cs.CV2023

Free-Form Composition Networks for Egocentric Action Recognition

Haoran Wang, Qinghua Cheng, Baosheng Yu +4

Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognit…

cs.CV20203 cited

Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples

Jinfeng Li, Weifeng Liu, Yicong Zhou +2

Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that eno…

cs.CV201910 cited

Hetero-Center Loss for Cross-Modality Person Re-Identification

Yuanxin Zhu, Zhao Yang, Li Wang +3

Cross-modality person re-identification is a challenging problem which retrieves a given pedestrian image in RGB modality among all the gallery images in infrared modality. The tas…

cs.CV201910 cited

Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More

Jingwen Ye, Yixin Ji, Xinchao Wang +3

In this paper, we investigate a novel deep-model reusing task. Our goal is to train a lightweight and versatile student model, without human-labelled annotations, that amalgamates…