27 citations · 43 across the 12 of their papers we have counts for
12 papers · 1 filter
Interactive Spatial-Frequency Fusion Mamba for Multi-Modal Image Fusion
Yixin Zhu, Long Lv, Pingping Zhang +5
Multi-Modal Image Fusion (MMIF) aims to combine images from different modalities to produce fused images, retaining texture details and preserving significant information. Recently…
X-ReID: Multi-granularity Information Interaction for Video-Based Visible-Infrared Person Re-Identification
Chenyang Yu, Xuehu Liu, Pingping Zhang +1
Large-scale vision-language models (e.g., CLIP) have recently achieved remarkable performance in retrieval tasks, yet their potential for Video-based Visible-Infrared Person Re-Ide…
MambaTrack3D: A State Space Model Framework for LiDAR-Based Object Tracking under High Temporal Variation
Shengjing Tian, Yinan Han, Xiantong Zhao +2
Dynamic outdoor environments with high temporal variation (HTV) pose significant challenges for 3D single object tracking in LiDAR point clouds. Existing memory-based trackers ofte…
AG-VPReID 2025: Aerial-Ground Video-based Person Re-identification Challenge Results
Kien Nguyen, Clinton Fookes, Sridha Sridharan +20
Person re-identification (ReID) across aerial and ground vantage points has become crucial for large-scale surveillance and public safety applications. Although significant progres…
Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification
Yuhao Wang, Pingping Zhang, Xuehu Liu +2
Person Re-identification (ReID) aims to retrieve the specific person across non-overlapping cameras, which greatly helps intelligent transportation systems. As we all know, Convolu…
MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt
Yuhao Wang, Xuehu Liu, Tianyu Yan +4
Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trai…
TF-CLIP: Learning Text-free CLIP for Video-based Person Re-Identification
Chenyang Yu, Xuehu Liu, Yingquan Wang +2
Large-scale language-image pre-trained models (e.g., CLIP) have shown superior performances on many cross-modal retrieval tasks. However, the problem of transferring the knowledge…
TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation
Yuhao Wang, Xuehu Liu, Pingping Zhang +3
Multi-spectral object Re-identification (ReID) aims to retrieve specific objects by leveraging complementary information from different image spectra. It delivers great advantages…
Video-based Person Re-identification with Long Short-Term Representation Learning
Xuehu Liu, Pingping Zhang, Huchuan Lu
Video-based person Re-Identification (V-ReID) aims to retrieve specific persons from raw videos captured by non-overlapped cameras. As a fundamental task, it spreads many multimedi…
Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification
Xuehu Liu, Chenyang Yu, Pingping Zhang +1
Advanced deep Convolutional Neural Networks (CNNs) have shown great success in video-based person Re-Identification (Re-ID). However, they usually focus on the most obvious regions…
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
Xuehu Liu, Pingping Zhang, Chenyang Yu +3
Video-based person re-identification (Re-ID) aims to retrieve video sequences of the same person under non-overlapping cameras. Previous methods usually focus on limited views, suc…
Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification
Xuehu Liu, Pingping Zhang, Chenyang Yu +2
Video-based person re-identification (Re-ID) aims to automatically retrieve video sequences of the same person under non-overlapping cameras. To achieve this goal, it is the key to…