9 papers
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…
Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking
Andong Lu, Yuanzhi Guo, Wanyu Wang +3
Current RGBT tracking methods often overlook the impact of fusion location on mitigating modality gap, which is key factor to effective tracking. Our analysis reveals that shallowe…
Towards General Multimodal Visual Tracking
Andong Lu, Mai Wen, Jinhu Wang +4
Existing multimodal tracking studies focus on bi-modal scenarios such as RGB-Thermal, RGB-Event, and RGB-Language. Although promising tracking performance is achieved through lever…
Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning
Andong Lu, Chenglong Li, Tianrui Zha +3
Prevalent nighttime person re-identification (ReID) methods typically combine image relighting and ReID networks in a sequential manner. However, their performance (recognition acc…
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba
Andong Lu, Wanyu Wang, Chenglong Li +2
Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, w…
Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks
Andong Lu, Jiacong Zhao, Chenglong Li +2
Current RGBT tracking research relies on the complete multi-modal input, but modal information might miss due to some factors such as thermal sensor self-calibration and data trans…