most citedTowards Privacy-Preserving Person Re-identification via Person Identify Shift

6 citations · 10 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20242 cited

DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion

Weicai Ye, Chenhao Ji, Zheng Chen +7

Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even images remains constr…

cs.CV2024

HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling

Yubin Wang, Xinyang Jiang, De Cheng +3

Prompt learning has become a prevalent strategy for adapting vision-language foundation models (VLMs) such as CLIP to downstream tasks. With the emergence of large language models…

cs.CV20241 cited

ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding

Yubin Wang, Xinyang Jiang, De Cheng +2

Video temporal grounding is an emerging topic aiming to identify specific clips within videos. In addition to pre-trained video models, contemporary methods utilize pre-trained vis…

cs.IR2024

PatSTEG: Modeling Formation Dynamics of Patent Citation Networks via The Semantic-Topological Evolutionary Graph

Ran Miao, Xueyu Chen, Liang Hu +4

Patent documents in the patent database (PatDB) are crucial for research, development, and innovation as they contain valuable technical information. However, PatDB presents a mult…

cs.CV20241 cited

DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification

Shuguang Dou, Xiangyang Jiang, Yuanpeng Tu +4

The paper introduces the Decouple Re-identificatiOn and human Parsing (DROP) method for occluded person re-identification (ReID). Unlike mainstream approaches using global features…

cs.CV2022

Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling

Boshen Zhang, Yuxi Li, Yuanpeng Tu +5

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to…