no code implementations • 11 May 2023 • Zixuan Ni, Longhui Wei, Siliang Tang, Yueting Zhuang, Qi Tian
Moreover, we empirically and theoretically demonstrate how SD leads to a performance decline for CLIP on cross-modal retrieval tasks.
1 code implementation • 12 Apr 2023 • Liping Bao, Longhui Wei, Xiaoyu Qiu, Wengang Zhou, Houqiang Li, Qi Tian
Recent researches on unsupervised person re-identification~(reID) have demonstrated that pre-training on unlabeled person images achieves superior performance on downstream reID tasks than pre-training on ImageNet.
no code implementations • 12 Mar 2023 • Juncheng Li, Minghe Gao, Longhui Wei, Siliang Tang, Wenqiao Zhang, Mengze Li, Wei Ji, Qi Tian, Tat-Seng Chua, Yueting Zhuang
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models.
no code implementations • 7 Mar 2023 • Jiacheng Li, Longhui Wei, Zongyuan Zhan, Xin He, Siliang Tang, Qi Tian, Yueting Zhuang
To better accelerate the generative transformers while keeping good generation quality, we propose Lformer, a semi-autoregressive text-to-image generation model.
1 code implementation • CVPR 2023 • Yunjie Tian, Lingxi Xie, Zhaozhi Wang, Longhui Wei, Xiaopeng Zhang, Jianbin Jiao, YaoWei Wang, Qi Tian, Qixiang Ye
In this paper, we present an integral pre-training framework based on masked image modeling (MIM).
1 code implementation • 4 Aug 2022 • Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, Siliang Tang
Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks.
1 code implementation • 3 Aug 2022 • Juncheng Li, Junlin Xie, Linchao Zhu, Long Qian, Siliang Tang, Wenqiao Zhang, Haochen Shi, Shengyu Zhang, Longhui Wei, Qi Tian, Yueting Zhuang
In this paper, we introduce a new task, named Temporal Emotion Localization in videos~(TEL), which aims to detect human emotions and localize their corresponding temporal boundaries in untrimmed videos with aligned subtitles.
1 code implementation • 2 Jun 2022 • Ming Tao, Bing-Kun Bao, Hao Tang, Fei Wu, Longhui Wei, Qi Tian
To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) which composes different editing modules dynamically for various editing requirements.
no code implementations • 10 Mar 2022 • Longhui Wei, Lingxi Xie, Wengang Zhou, Houqiang Li, Qi Tian
Recently, masked image modeling (MIM) has become a promising direction for visual pre-training.
no code implementations • CVPR 2022 • Yadong Ding, Yu Wu, Chengyue Huang, Siliang Tang, Yi Yang, Longhui Wei, Yueting Zhuang, Qi Tian
Existing NAS-based meta-learning methods apply a two-stage strategy, i. e., first searching architectures and then re-training meta-weights on the searched architecture.
no code implementations • 26 Jul 2021 • Zixuan Ni, Haizhou Shi, Siliang Tang, Longhui Wei, Qi Tian, Yueting Zhuang
After investigating existing strategies, we observe that there is a lack of study on how to prevent the inter-phase confusion.
1 code implementation • NeurIPS 2021 • Xu Luo, Longhui Wei, Liangjian Wen, Jinrong Yang, Lingxi Xie, Zenglin Xu, Qi Tian
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL).
no code implementations • 1 Jun 2021 • Longhui Wei, Lingxi Xie, Wengang Zhou, Houqiang Li, Qi Tian
By simply pulling the different augmented views of each image together or other novel mechanisms, they can learn much unsupervised knowledge and significantly improve the transfer performance of pre-training models.
no code implementations • 28 May 2021 • Lingxi Xie, Xiaopeng Zhang, Longhui Wei, Jianlong Chang, Qi Tian
This is an opinion paper.
5 code implementations • ICCV 2021 • Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, Qi Tian
The past year has witnessed the rapid development of applying the Transformer module to vision problems.
Ranked #455 on
Image Classification
on ImageNet
no code implementations • 30 Mar 2021 • Tianyu Zhang, Longhui Wei, Lingxi Xie, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian
Recently, the Transformer module has been transplanted from natural language processing to computer vision.
1 code implementation • CVPR 2021 • Tianyu Zhang, Lingxi Xie, Longhui Wei, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian
The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains.
no code implementations • 19 Nov 2020 • Xinyue Huo, Lingxi Xie, Longhui Wei, Xiaopeng Zhang, Hao Li, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.
no code implementations • 17 Nov 2020 • Longhui Wei, Lingxi Xie, Jianzhong He, Jianlong Chang, Xiaopeng Zhang, Wengang Zhou, Houqiang Li, Qi Tian
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
no code implementations • 4 Aug 2020 • Lingxi Xie, Xin Chen, Kaifeng Bi, Longhui Wei, Yuhui Xu, Zhengsu Chen, Lanfei Wang, An Xiao, Jianlong Chang, Xiaopeng Zhang, Qi Tian
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
1 code implementation • 7 Jul 2020 • Kaifeng Bi, Lingxi Xie, Xin Chen, Longhui Wei, Qi Tian
There has been a large literature of neural architecture search, but most existing work made use of heuristic rules that largely constrained the search flexibility.
no code implementations • 17 Apr 2020 • Xin Chen, Lingxi Xie, Jun Wu, Longhui Wei, Yuhui Xu, Qi Tian
We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal.
1 code implementation • CVPR 2020 • Zhengsu Chen, Jianwei Niu, Lingxi Xie, Xuefeng Liu, Longhui Wei, Qi Tian
Automatic designing computationally efficient neural networks has received much attention in recent years.
1 code implementation • ECCV 2020 • Longhui Wei, An Xiao, Lingxi Xie, Xin Chen, Xiaopeng Zhang, Qi Tian
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization.
Ranked #171 on
Image Classification
on ImageNet
1 code implementation • ECCV 2020 • Zijie Zhuang, Longhui Wei, Lingxi Xie, Tianyu Zhang, Hengheng Zhang, Haozhe Wu, Haizhou Ai, Qi Tian
The fundamental difficulty in person re-identification (ReID) lies in learning the correspondence among individual cameras.
Ranked #15 on
Unsupervised Domain Adaptation
on Duke to Market
Direct Transfer Person Re-identification
Domain Adaptive Person Re-Identification
+2
1 code implementation • 25 Oct 2019 • Kaifeng Bi, Changping Hu, Lingxi Xie, Xin Chen, Longhui Wei, Qi Tian
Our approach bridges the gap from two aspects, namely, amending the estimation on the architectural gradients, and unifying the hyper-parameter settings in the search and re-training stages.
1 code implementation • 24 Sep 2019 • Tianyu Zhang, Lingxi Xie, Longhui Wei, Yongfei Zhang, Bo Li, Qi Tian
Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera.
25 code implementations • CVPR 2018 • Longhui Wei, Shiliang Zhang, Wen Gao, Qi Tian
Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e. g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network.
Ranked #10 on
Unsupervised Person Re-Identification
on DukeMTMC-reID
(Rank-10 metric)
no code implementations • 13 Sep 2017 • Longhui Wei, Shiliang Zhang, Hantao Yao, Wen Gao, Qi Tian
Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively.
Ranked #86 on
Person Re-Identification
on Market-1501