2 citations · 2 across the 3 of their papers we have counts for
7 papers · 1 filter
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
Weizhe Kong, Xiao Wang, Ruichong Gao +5
Pedestrian Attribute Recognition (PAR) is an indispensable task in human-centered research and has made great progress in recent years with the development of deep neural networks.…
CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training Framework
Wentao Wu, Xiao Wang, Chenglong Li +4
Event cameras have attracted increasing attention in recent years due to their advantages in high dynamic range, high temporal resolution, low power consumption, and low latency. S…
RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework
Xiao Wang, Haiyang Wang, Shiao Wang +5
Existing pedestrian attribute recognition methods are generally developed based on RGB frame cameras. However, these approaches are constrained by the limitations of RGB cameras, s…
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework
Jiandong Jin, Xiao Wang, Qian Zhu +2
Pedestrian Attribute Recognition (PAR) is one of the indispensable tasks in human-centered research. However, existing datasets neglect different domains (e.g., environments, times…
An Empirical Study of Mamba-based Pedestrian Attribute Recognition
Xiao Wang, Weizhe Kong, Jiandong Jin +5
Current strong pedestrian attribute recognition models are developed based on Transformer networks, which are computationally heavy. Recently proposed models with linear complexity…
Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition
Xiao Wang, Qian Zhu, Jiandong Jin +5
Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image, however, the performance is unreliable in challenging scenarios, such as he…