1 citations · 4 across the 13 of their papers we have counts for
13 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…
Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network
Kunpeng Wang, Keke Chen, Chenglong Li +2
Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from una…
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
Andong Lu, Jiacong Zhao, Chenglong Li +2
Modality gap between RGB and thermal infrared (TIR) images is a crucial issue but often overlooked in existing RGBT tracking methods. It can be observed that modality gap mainly li…
Cross-modulated Attention Transformer for RGBT Tracking
Yun Xiao, Jiacong Zhao, Andong Lu +4
Existing Transformer-based RGBT trackers achieve remarkable performance benefits by leveraging self-attention to extract uni-modal features and cross-attention to enhance multi-mod…
Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance
Kunpeng Wang, Danying Lin, Chenglong Li +2
Although most existing multi-modal salient object detection (SOD) methods demonstrate effectiveness through training models from scratch, the limited multi-modal data hinders these…
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…
AFter: Attention-based Fusion Router for RGBT Tracking
Andong Lu, Wanyu Wang, Chenglong Li +2
Multi-modal feature fusion as a core investigative component of RGBT tracking emerges numerous fusion studies in recent years. However, existing RGBT tracking methods widely adopt…
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens
Dengdi Sun, Yajie Pan, Andong Lu +2
Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced h…
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…
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…
Unified-modal Salient Object Detection via Adaptive Prompt Learning
Kunpeng Wang, Chenglong Li, Zhengzheng Tu +2
Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing comp…