Search Results for author: Tianyang Xu

Found 37 papers, 19 papers with code

C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition

1 code implementation8 Jul 2024 Rongchang Li, ZhenHua Feng, Tianyang Xu, Linze Li, Xiao-Jun Wu, Muhammad Awais, Sara Atito, Josef Kittler

For evaluating the task, we construct a new benchmark, Something-composition (Sth-com), based on the widely used Something-Something V2 dataset.

Action Recognition

SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation

1 code implementation18 Jun 2024 Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao

Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting in several high-profile lawsuits.

Text Generation

SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales

1 code implementation31 May 2024 Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, Jing Gao

Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications.

TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking

1 code implementation8 May 2024 Pengcheng Shao, Tianyang Xu, Zhangyong Tang, Linze Li, Xiao-Jun Wu, Josef Kittler

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 about the scene motion.

Visual Object Tracking

An Improved Graph Pooling Network for Skeleton-Based Action Recognition

no code implementations25 Apr 2024 Cong Wu, Xiao-Jun Wu, Tianyang Xu, Josef Kittler

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.

Action Recognition Skeleton Based Action Recognition

Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs

1 code implementation1 Apr 2024 Xiaoze Liu, Feijie Wu, Tianyang Xu, Zhuo Chen, Yichi Zhang, Xiaoqian Wang, Jing Gao

In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset.

Knowledge Graphs

TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion

1 code implementation21 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.

Image Quality Assessment Language Modelling

Generative-based Fusion Mechanism for Multi-Modal Tracking

1 code implementation4 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.

Rgb-T Tracking

Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System

1 code implementation27 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).

Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories

1 code implementation8 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.

Domain Adaptation

FusionBooster: A Unified Image Fusion Boosting Paradigm

1 code implementation10 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.

LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images

1 code implementation11 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.

Representation Learning

Adaptive Riemannian Metrics on SPD Manifolds

no code implementations26 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.

LabelPrompt: Effective Prompt-based Learning for Relation Classification

no code implementations16 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.

Classification Contrastive Learning +3

SDA-$x$Net: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation

1 code implementation21 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.

RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking

1 code implementation21 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.

Visual Object Tracking

DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification

no code implementations16 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.

Discriminative Supervised Subspace Learning for Cross-modal Retrieval

no code implementations26 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.

Cross-Modal Retrieval Retrieval +2

Riemannian Local Mechanism for SPD Neural Networks

1 code implementation25 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.


Unsupervised Image Fusion Method based on Feature Mutual Mapping

no code implementations25 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.

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network

no code implementations25 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

A Survey for Deep RGBT Tracking

no code implementations23 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.

Visual Object Tracking

Temporal Aggregation for Adaptive RGBT Tracking

1 code implementation22 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.

Visual Object Tracking

Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking

1 code implementation21 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.

Object Visual Object Tracking

Video Is Graph: Structured Graph Module for Video Action Recognition

no code implementations12 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.

Action Recognition Temporal Action Localization

PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion

no code implementations29 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.

Image Classification Image Reconstruction

Reinforced Generative Adversarial Network for Abstractive Text Summarization

no code implementations31 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 Decoder +1

AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking

no code implementations27 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.

Benchmarking One-Shot Learning +1

Adaptive Distraction Context Aware Tracking Based on Correlation Filter

no code implementations24 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.

An Accelerated Correlation Filter Tracker

no code implementations5 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.

Benchmarking Visual Object Tracking

Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

1 code implementation30 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.

Benchmarking feature selection +2

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