30 citations · 43 across the 7 of their papers we have counts for
7 papers
VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models
Wentao Wu, Fanghua Hong, Xiao Wang +2
Existing vehicle detectors are usually obtained by training a typical detector (e.g., YOLO, RCNN, DETR series) on vehicle images based on a pre-trained backbone (e.g., ResNet, ViT)…
Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception
Xiao Wang, Wentao Wu, Chenglong Li +4
Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-trai…
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
Xiao Wang, Jiandong Jin, Chenglong Li +3
Existing pedestrian attribute recognition (PAR) algorithms adopt pre-trained CNN (e.g., ResNet) as their backbone network for visual feature learning, which might obtain sub-optima…
SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm
Jiandong Jin, Xiao Wang, Yin Lin +4
Current pedestrian attribute recognition (PAR) algorithms use multi-label or multi-task learning frameworks with specific classification heads. These models often struggle with imb…
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity
Xiao Wang, Ziliang Chen, Rui Yang +2
In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification. It is…
Pedestrian Attribute Recognition: A Survey
Xiao Wang, Shaofei Zheng, Rui Yang +4
Recognizing pedestrian attributes is an important task in the computer vision community due to it plays an important role in video surveillance. Many algorithms have been proposed…
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Ziliang Chen, Keze Wang, Xiao Wang +3
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS)…