421 citations · 586 across the 17 of their papers we have counts for
17 papers
SDA-Net: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation
Qingbei Guo, Xiao-Jun Wu, Zhiquan Feng +2
Existing multi-scale solutions lead to a risk of just increasing the receptive field sizes while neglecting small receptive fields. Thus, it is a challenging problem to effectively…
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
Xue-Feng Zhu, Tianyang Xu, Zhangyong Tang +5
RGB-D object tracking has attracted considerable attention recently, achieving promising performance thanks to the symbiosis between visual and depth channels. However, given a lim…
DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification
Rui Wang, Xiao-Jun Wu, Ziheng Chen +2
Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric pos…
TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network
Dongyu Rao, Xiao-Jun Wu, Tianyang Xu
The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range de…
Unsupervised Image Fusion Method based on Feature Mutual Mapping
Dongyu Rao, Xiao-Jun Wu, Tianyang Xu +1
Deep learning-based image fusion approaches have obtained wide attention in recent years, achieving promising performance in terms of visual perception. However, the fusion module…
A Survey for Deep RGBT Tracking
Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu
Visual object tracking with the visible (RGB) and thermal infrared (TIR) electromagnetic waves, shorted in RGBT tracking, recently draws increasing attention in the tracking commun…
Temporal Aggregation for Adaptive RGBT Tracking
Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu
Visual object tracking with RGB and thermal infrared (TIR) spectra available, shorted in RGBT tracking, is a novel and challenging research topic which draws increasing attention n…
Discriminative Supervised Subspace Learning for Cross-modal Retrieval
Haoming Zhang, Xiao-Jun Wu, Tianyang Xu +1
Nowadays the measure between heterogeneous data is still an open problem for cross-modal retrieval. The core of cross-modal retrieval is how to measure the similarity between diffe…
Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking
Zhangyong Tang, Tianyang Xu, Hui Li +3
We address the problem of multi-modal object tracking in video and explore various options of fusing the complementary information conveyed by the visible (RGB) and thermal infrare…
Visual Object Tracking on Multi-modal RGB-D Videos: A Review
Xue-Feng Zhu, Tianyang Xu, Xiao-Jun Wu
The development of visual object tracking has continued for decades. Recent years, as the wide accessibility of the low-cost RGBD sensors, the task of visual object tracking on RGB…
PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion
Yu Fu, TianYang Xu, XiaoJun Wu +1
The Transformer architecture has witnessed a rapid development in recent years, outperforming the CNN architectures in many computer vision tasks, as exemplified by the Vision Tran…
Reinforced Generative Adversarial Network for Abstractive Text Summarization
Tianyang Xu, Chunyun Zhang
Sequence-to-sequence models provide a viable new approach to generative summarization, allowing models that are no longer limited to simply selecting and recombining sentences from…
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking
Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu +1
Siamese approaches have achieved promising performance in visual object tracking recently. The key to the success of Siamese trackers is to learn appearance-invariant feature embed…
Adaptive Distraction Context Aware Tracking Based on Correlation Filter
Fei Feng, Xiao-Jun Wu, Tianyang Xu +2
The Discriminative Correlation Filter (CF) uses a circulant convolution operation to provide several training samples for the design of a classifier that can distinguish the target…
An Accelerated Correlation Filter Tracker
Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu +1
Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and…
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu +1
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perfo…
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu +1
With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions…