2 citations · 5 across the 11 of their papers we have counts for
11 papers
Parameter-Dynamic Adaptive Fusion and Calibration Network for RGBT Tracking
Zhaoding Ding, Chenglong Li, Jiandong Jin +2
Existing RGBT trackers typically employ fusion functions with fixed parameters across different targets and scenarios. Although dynamic-architecture methods improve fusion flexibil…
Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking
Andong Lu, Ziyi Zha, Jiandong Jin +4
Missing modalities in RGBT tracking often lead to incomplete and unstable multimodal feature representations that greatly degrade the performance. Existing methods typically attemp…
Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark
Yun Xiao, Zhihong Hong, Jiandong Jin +3
Unmanned Aerial Vehicle (UAV) object tracking has emerged as a popular research field with broad practical applications. Modern UAVs are increasingly equipped with both visible lig…
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…
Pre-training on High Definition X-ray Images: An Experimental Study
Xiao Wang, Yuehang Li, Wentao Wu +5
Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 224).…
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…
Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Dong Li, Jiandong Jin, Yuhao Zhang +5
Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to i…