15 citations · 22 across the 24 of their papers we have counts for
25 papers
Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach
Yingjie Dai, Tianyang Xu, Yanglin Deng +2
Skeleton-based action recognition has achieved remarkable success by exploiting joint coordinates and their topological connections, yet prevailing methods overwhelmingly assume co…
EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment
Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu +4
Evaluation is essential in image fusion research, yet most existing metrics are directly borrowed from other vision tasks without proper adaptation. These traditional metrics, ofte…
Beyond Strict Pairing: Arbitrarily Paired Training for High-Performance Infrared and Visible Image Fusion
Yanglin Deng, Tianyang Xu, Chunyang Cheng +3
Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures. Existing solutions predominantly rely on…
Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models
Yuanbo Li, Tianyang Xu, Cong Hu +3
The rapid progress of Multi-Modal Large Language Models (MLLMs) has significantly advanced downstream applications. However, this progress also exposes serious transferable adversa…
Towards Highly Transferable Vision-Language Attack via Semantic-Augmented Dynamic Contrastive Interaction
Yuanbo Li, Tianyang Xu, Cong Hu +3
With the rapid advancement and widespread application of vision-language pre-training (VLP) models, their vulnerability to adversarial attacks has become a critical concern. In gen…
Learning Progressive Adaptation for Multi-Modal Tracking
He Wang, Tianyang Xu, Zhangyong Tang +2
Due to the limited availability of paired multi-modal data, multi-modal trackers are typically built by adopting pre-trained RGB models with parameter-efficient fine-tuning modules…
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…
Serial Over Parallel: Learning Continual Unification for Multi-Modal Visual Object Tracking and Benchmarking
Zhangyong Tang, Tianyang Xu, Xuefeng Zhu +4
Unifying multiple multi-modal visual object tracking (MMVOT) tasks draws increasing attention due to the complementary nature of different modalities in building robust tracking sy…
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…
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…
Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections
Youwei Zhou, Tianyang Xu, Cong Wu +2
The shared topology of human skeletons motivated the recent investigation of graph convolutional network (GCN) solutions for action recognition. However, most of the existing GCNs…
SMLNet: A SPD Manifold Learning Network for Infrared and Visible Image Fusion
Huan Kang, Hui Li, Tianyang Xu +4
Euclidean representation learning methods have achieved promising results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. Ho…
Dynamic Subframe Splitting and Spatio-Temporal Motion Entangled Sparse Attention for RGB-E Tracking
Pengcheng Shao, Tianyang Xu, Xuefeng Zhu +2
Event-based bionic camera asynchronously captures dynamic scenes with high temporal resolution and high dynamic range, offering potential for the integration of events and RGB unde…
MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusion
Yanglin Deng, Tianyang Xu, Chunyang Cheng +2
In recent years, Multi-Modality Image Fusion (MMIF) has been applied to many fields, which has attracted many scholars to endeavour to improve the fusion performance. However, the…
TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking
Pengcheng Shao, Tianyang Xu, Zhangyong Tang +3
There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative abou…
An Improved Graph Pooling Network for Skeleton-Based Action Recognition
Cong Wu, Xiao-Jun Wu, Tianyang Xu +1
Pooling is a crucial operation in computer vision, yet the unique structure of skeletons hinders the application of existing pooling strategies to skeleton graph modelling. In this…
BusReF: Infrared-Visible images registration and fusion focus on reconstructible area using one set of features
Zeyang Zhang, Hui Li, Tianyang Xu +2
In a scenario where multi-modal cameras are operating together, the problem of working with non-aligned images cannot be avoided. Yet, existing image fusion algorithms rely heavily…
TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion
Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu +4
Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images. However, the difference in th…
Riemannian Self-Attention Mechanism for SPD Networks
Rui Wang, Xiao-Jun Wu, Hui Li +1
Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the dat…
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition
Cong Wu, Xiao-Jun Wu, Josef Kittler +4
Contrastive learning has achieved great success in skeleton-based action recognition. However, most existing approaches encode the skeleton sequences as entangled spatiotemporal re…