15 citations · 35 across the 35 of their papers we have counts for
9 papers · 1 filter
A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles
Tianyang Xu, Jinjie Gu, Xuefeng Zhu +2
With the proliferation of low altitude unmanned aerial vehicles (UAVs), visual multi-object tracking is becoming a critical security technology, demanding significant robustness ev…
MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
Peng Xu, Shengwu Xiong, Jiajun Zhang +125
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We…
Collaborating Vision, Depth, and Thermal Signals for Multi-Modal Tracking: Dataset and Algorithm
Xue-Feng Zhu, Tianyang Xu, Yifan Pan +5
Existing multi-modal object tracking approaches primarily focus on dual-modal paradigms, such as RGB-Depth or RGB-Thermal, yet remain challenged in complex scenarios due to limited…
Omni Survey for Multimodality Analysis in Visual Object Tracking
Zhangyong Tang, Tianyang Xu, Xuefeng Zhu +6
The development of smart cities has led to the generation of massive amounts of multi-modal data in the context of a range of tasks that enable a comprehensive monitoring of the sm…
GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion
Huan Kang, Hui Li, Xiao-Jun Wu +4
In the field of image fusion, promising progress has been made by modeling data from different modalities as linear subspaces. However, in practice, the source images are often loc…
Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
Hongyao Chen, Tianyang Xu, Xiaojun Wu +1
Batch Normalisation (BN) is widely used in conventional deep neural network training to harmonise the input-output distributions for each batch of data. However, federated learning…
One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
Chunyang Cheng, Tianyang Xu, Zhenhua Feng +7
Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms. In contrast, we pro…
Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose Estimation
Tianyang Xu, Jiyong Rao, Xiaoning Song +2
General mammal pose estimation is an important and challenging task in computer vision, which is essential for understanding mammal behaviour in real-world applications. However, e…
UASTrack: A Unified Adaptive Selection Framework with Modality-Customization in Single Object Tracking
He Wang, Tianyang Xu, Zhangyong Tang +2
Multi-modal tracking is essential in single-object tracking (SOT), as different sensor types contribute unique capabilities to overcome challenges caused by variations in object ap…