activity
20192025
most citedSpatiotemporal Transformer for Video-based Person Re-identification

30 citations · 35 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2025

Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment

Ying Ba, Tianyu Zhang, Yalong Bai +4

Contemporary image generation systems have achieved high fidelity and superior aesthetic quality beyond basic text-image alignment. However, existing evaluation frameworks have fai…

cs.CV2024

Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing

Wenyi Mo, Tianyu Zhang, Yalong Bai +2

Text-guided image generation and editing using diffusion models have achieved remarkable advancements. Among these, tuning-free methods have gained attention for their ability to p…

cs.CV202130 cited

Spatiotemporal Transformer for Video-based Person Re-identification

Tianyu Zhang, Longhui Wei, Lingxi Xie +4

Recently, the Transformer module has been transplanted from natural language processing to computer vision. This paper applies the Transformer to video-based person re-identificati…

cs.CV20205 cited

UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification

Tianyu Zhang, Lingxi Xie, Longhui Wei +4

The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a no…

cs.CV2020

Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization

Zijie Zhuang, Longhui Wei, Lingxi Xie +5

The fundamental difficulty in person re-identification (ReID) lies in learning the correspondence among individual cameras. It strongly demands costly inter-camera annotations, yet…

cs.CV2019

Background Segmentation for Vehicle Re-Identification

Mingjie Wu, Yongfei Zhang, Tianyu Zhang +1

Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance.Prior works focus on extracting discriminative features from visual appeara…