no code implementations • 30 Dec 2023 • Zeyang Zhang, Hui Li, Tianyang Xu, XiaoJun Wu, Josef Kittler
We focus on Infrared-Visible image registration and fusion task (IVRF).
1 code implementation • 21 Dec 2023 • Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu, Hui Li, Xi Li, Zhangyong Tang, Josef Kittler
Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images.
no code implementations • 11 Sep 2023 • Cong Wu, Xiao-Jun Wu, Josef Kittler, Tianyang Xu, Sara Atito, Muhammad Awais, ZhenHua Feng
Contrastive learning has achieved great success in skeleton-based action recognition.
1 code implementation • 4 Sep 2023 • Zhangyong Tang, Tianyang Xu, XueFeng Zhu, Xiao-Jun Wu, Josef Kittler
In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking.
1 code implementation • 27 Jun 2023 • Xue-Feng Zhu, Tianyang Xu, Jian Zhao, Jia-Wei Liu, Kai Wang, Gang Wang, Jianan Li, Qiang Wang, Lei Jin, Zheng Zhu, Junliang Xing, Xiao-Jun Wu
Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i. e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance).
1 code implementation • 8 Jun 2023 • Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.
no code implementations • 12 May 2023 • Jian Zhao, Jianan Li, Lei Jin, Jiaming Chu, Zhihao Zhang, Jun Wang, Jiangqiang Xia, Kai Wang, Yang Liu, Sadaf Gulshad, Jiaojiao Zhao, Tianyang Xu, XueFeng Zhu, Shihan Liu, Zheng Zhu, Guibo Zhu, Zechao Li, Zheng Wang, Baigui Sun, Yandong Guo, Shin ichi Satoh, Junliang Xing, Jane Shen Shengmei
Second, we set up two tracks for the first time, i. e., Anti-UAV Tracking and Anti-UAV Detection & Tracking.
1 code implementation • 10 May 2023 • Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu, Hui Li, Xi Li, Josef Kittler
We argue that there is a scope to improve the fusion performance with the help of the FusionBooster, a model specifically designed for the fusion task.
1 code implementation • 11 Apr 2023 • Hui Li, Tianyang Xu, Xiao-Jun Wu, Jiwen Lu, Josef Kittler
In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model.
no code implementations • 26 Mar 2023 • Ziheng Chen, Yue Song, Tianyang Xu, Zhiwu Huang, Xiao-Jun Wu, Nicu Sebe
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity of encoding underlying structural correlation in data.
no code implementations • 16 Feb 2023 • Wenjie Zhang, Xiaoning Song, ZhenHua Feng, Tianyang Xu, XiaoJun Wu
Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e. g.} \textit{``org:founded\_by}'') is difficult.
1 code implementation • 21 Sep 2022 • Qingbei Guo, Xiao-Jun Wu, Zhiquan Feng, Tianyang Xu, Cong Hu
To tackle this issue, we first introduce a new attention dimension, i. e., depth, in addition to existing attention dimensions such as channel, spatial, and branch, and present a novel selective depth attention network to symmetrically handle multi-scale objects in various vision tasks.
1 code implementation • 21 Aug 2022 • Xue-Feng Zhu, Tianyang Xu, Zhangyong Tang, Zucheng Wu, Haodong Liu, Xiao Yang, Xiao-Jun Wu, Josef Kittler
To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset.
no code implementations • 16 Jun 2022 • Rui Wang, Xiao-Jun Wu, Ziheng Chen, Tianyang Xu, Josef Kittler
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 positive definite (SPD) manifold.
no code implementations • 26 Jan 2022 • Haoming Zhang, Xiao-Jun Wu, Tianyang Xu, Donglin Zhang
Thirdly, we introduce a similarity preservation term, thus our model can compensate for the shortcomings of insufficient use of discriminative data and better preserve the semantically structural information within each modality.
no code implementations • 25 Jan 2022 • 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.
Generative Adversarial Network Infrared And Visible Image Fusion
no code implementations • 25 Jan 2022 • Dongyu Rao, Xiao-Jun Wu, Tianyang Xu, Guoyang Chen
We propose a feature mutual mapping fusion module and dual-branch multi-scale autoencoder.
1 code implementation • 25 Jan 2022 • Ziheng Chen, Tianyang Xu, Xiao-Jun Wu, Rui Wang, Zhiwu Huang, Josef Kittler
The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas.
no code implementations • 23 Jan 2022 • Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu
This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.
no code implementations • 23 Jan 2022 • Xue-Feng Zhu, Tianyang Xu, Xiao-Jun Wu
The development of visual object tracking has continued for decades.
1 code implementation • 22 Jan 2022 • Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu
Specifically, different from traditional Siamese trackers, which only obtain one search image during the process of picking up template-search image pairs, an extra search sample adjacent to the original one is selected to predict the temporal transformation, resulting in improved robustness of tracking performance. As for multi-modal tracking, constrained to the limited RGBT datasets, the adaptive fusion sub-network is appended to our method at the decision level to reflect the complementary characteristics contained in two modalities.
1 code implementation • 21 Jan 2022 • Zhangyong Tang, Tianyang Xu, Hui Li, Xiao-Jun Wu, XueFeng Zhu, Josef Kittler
The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation.
no code implementations • 12 Oct 2021 • Rongchang Li, Xiao-Jun Wu, Tianyang Xu
In this paper, we first propose to transform a video sequence into a graph to obtain direct long-term dependencies among temporal frames.
no code implementations • 29 Jul 2021 • Yu Fu, Tianyang Xu, XiaoJun Wu, Josef Kittler
In this paper, we propose a Patch Pyramid Transformer(PPT) to effectively address the above issues. Specifically, we first design a Patch Transformer to transform the image into a sequence of patches, where transformer encoding is performed for each patch to extract local representations.
no code implementations • 31 May 2021 • Tianyang Xu, Chunyun Zhang
However, these models have three drawbacks: their grasp of the details of the original text is often inaccurate, and the text generated by such models often has repetitions, while it is difficult to handle words that are beyond the word list.
Abstractive Text Summarization Generative Adversarial Network
no code implementations • 27 May 2020 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
To this end, we propose a failure-aware system, realised by a Quality Prediction Network (QPN), based on convolutional and LSTM modules in the decision stage, enabling online reporting of potential tracking failures.
no code implementations • 24 Dec 2019 • Fei Feng, Xiao-Jun Wu, Tianyang Xu, Josef Kittler, Xue-Feng Zhu
In the response map obtained for the previous frame by the CF algorithm, we adaptively find the image blocks that are similar to the target and use them as negative samples.
no code implementations • 5 Dec 2019 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
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 robust deep neural network features.
1 code implementation • ICCV 2019 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking.
Ranked #1 on Visual Object Tracking on VOT2017
1 code implementation • 30 Jul 2018 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold.