no code implementations • 29 Dec 2022 • Tianzhu Zhang, Han Qiu, Gabriele Castellano, Myriana Rifai, Chung Shue Chen, Fabio Pianese
This paper aims to provide a comprehensive survey on log parsing.
no code implementations • ECCV 2022 • Kongzhu Jiang, Tianzhu Zhang, Xiang Liu, Bingqiao Qian, Yongdong Zhang, Feng Wu ;
To alleviate the above issues, we propose a novel Cross-Modality Transformer (CMT) to jointly explore a modality-level alignment module and an instance-level module for VI-ReID.
1 code implementation • CVPR 2022 • Jinsheng Wang, Yinchao Ma, Shaofei Huang, Tianrui Hui, Fei Wang, Chen Qian, Tianzhu Zhang
Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes.
Ranked #3 on Lane Detection on TuSimple (F1 score metric)
no code implementations • CVPR 2022 • Jiamin Wu, Tianzhu Zhang, Zhe Zhang, Feng Wu, Yongdong Zhang
To address this issue, we propose an end-to-end Motion-modulated Temporal Fragment Alignment Network (MTFAN) by jointly exploring the task-specific motion modulation and the multi-level temporal fragment alignment for Few-Shot Action Recognition (FSAR).
no code implementations • 23 Nov 2021 • Pengfei Zhu, Hongtao Yu, Kaihua Zhang, Yu Wang, Shuai Zhao, Lei Wang, Tianzhu Zhang, QinGhua Hu
To address this issue, segmentation-based trackers have been proposed that employ per-pixel matching to improve the tracking performance of deformable objects effectively.
1 code implementation • ICCV 2021 • Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Junjie Yan, Wanli Ouyang
In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages.
no code implementations • CVPR 2021 • Rui Sun, Yihao Li, Tianzhu Zhang, Zhendong Mao, Feng Wu, Yongdong Zhang
First, to the best of our knowledge, this is the first work to formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder.
no code implementations • CVPR 2021 • Wenfei Yang, Tianzhu Zhang, Xiaoyuan Yu, Tian Qi, Yongdong Zhang, Feng Wu
To alleviate this problem, we propose a novel Uncertainty Guided Collaborative Training (UGCT) strategy, which mainly includes two key designs: (1) The first design is an online pseudo label generation module, in which the RGB and FLOW streams work collaboratively to learn from each other.
no code implementations • CVPR 2021 • Yulin Li, Jianfeng He, Tianzhu Zhang, Xiang Liu, Yongdong Zhang, Feng Wu
To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoderdecoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder.
no code implementations • CVPR 2021 • Wang Luo, Tianzhu Zhang, Wenfei Yang, Jingen Liu, Tao Mei, Feng Wu, Yongdong Zhang
In this paper, we present an Action Unit Memory Network (AUMN) for weakly supervised temporal action localization, which can mitigate the above two challenges by learning an action unit memory bank.
Ranked #5 on Weakly Supervised Action Localization on THUMOS14
Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1
no code implementations • ICCV 2021 • Meng Meng, Tianzhu Zhang, Qi Tian, Yongdong Zhang, Feng Wu
To the best of our knowledge, this is the first work that can achieve remarkable performance for both tasks by optimizing them jointly via FAM for WSOL.
no code implementations • ICCV 2021 • Weiwei Feng, Baoyuan Wu, Tianzhu Zhang, Yong Zhang, Yongdong Zhang
To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images.
no code implementations • ICCV 2021 • Jiamin Wu, Tianzhu Zhang, Yongdong Zhang, Feng Wu
The task-aware part filters can adapt to any individual task and automatically mine task-related local parts even for an unseen task.
1 code implementation • 3 Aug 2020 • Yehui Yang, Fangxin Shang, Binghong Wu, Dalu Yang, Lei Wang, Yanwu Xu, Wensheng Zhang, Tianzhu Zhang
As a result, it exploits more discriminative features for DR grading.
1 code implementation • CVPR 2020 • Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, Yongdong Zhang
The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase.
Ranked #13 on Cross-Modal Retrieval on Flickr30k
no code implementations • CVPR 2020 • Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, Nenghai Yu
In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the re-identification performance.
Cross-Modality Person Re-identification Person Re-Identification
1 code implementation • ICCV 2019 • Guan'an Wang, Tianzhu Zhang, Jian Cheng, Si Liu, Yang Yang, Zeng-Guang Hou
First, it can exploit pixel alignment and feature alignment jointly.
Cross-Modality Person Re-identification Person Re-Identification +1
1 code implementation • Proceedings of the AAAI Conference on Artificial Intelligence 2019 • Junyu Gao, Tianzhu Zhang, Changsheng Xu
To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch.
Ranked #4 on Zero-Shot Action Recognition on Olympics
no code implementations • 25 May 2019 • Ting-Ting Xie, Xiaoshan Yang, Tianzhu Zhang, Changsheng Xu, Ioannis Patras
Temporal action localization has recently attracted significant interest in the Computer Vision community.
no code implementations • 25 Nov 2018 • Xiao Wang, Chenglong Li, Rui Yang, Tianzhu Zhang, Jin Tang, Bin Luo
To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues.
no code implementations • CVPR 2018 • Feifei Zhang, Tianzhu Zhang, Qirong Mao, Changsheng Xu
First, the encoder-decoder structure of the generator can learn a generative and discriminative identity representation for face images.
no code implementations • CVPR 2017 • Tianzhu Zhang, Changsheng Xu, Ming-Hsuan Yang
In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking.
no code implementations • CVPR 2016 • Adel Bibi, Tianzhu Zhang, Bernard Ghanem
In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D.
no code implementations • CVPR 2016 • Si Liu, Tianzhu Zhang, Xiaochun Cao, Changsheng Xu
In this paper, we propose a novel structural correlation filter (SCF) model for robust visual tracking.
no code implementations • CVPR 2016 • Tianzhu Zhang, Adel Bibi, Bernard Ghanem
Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework.
no code implementations • CVPR 2015 • Tianzhu Zhang, Si Liu, Changsheng Xu, Shuicheng Yan, Bernard Ghanem, Narendra Ahuja, Ming-Hsuan Yang
Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates.
no code implementations • CVPR 2014 • Tianzhu Zhang, Kui Jia, Changsheng Xu, Yi Ma, Narendra Ahuja
The proposed part matching tracker (PMT) has a number of attractive properties.
no code implementations • 31 Mar 2014 • Kui Jia, Tsung-Han Chan, Zinan Zeng, Shenghua Gao, Gang Wang, Tianzhu Zhang, Yi Ma
The task is to identify the inlier features and establish their consistent correspondences across the image set.