1 code implementation • 21 Nov 2023 • Xiu-Shen Wei, Yang shen, Xuhao Sun, Peng Wang, Yuxin Peng
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i. e., the same sub-category labels) highest based on the fine-grained details in the query.
2 code implementations • 14 Oct 2023 • Jiabei He, Yang shen, Xiu-Shen Wei, Ye Wu
However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field.
1 code implementation • 19 Jul 2023 • Feiran Hu, Peng Wang, Yangyang Li, Chenlong Duan, Zijian Zhu, Fei Wang, Faen Zhang, Yong Li, Xiu-Shen Wei
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata.
3 code implementations • CVPR 2023 • Yang shen, Xuhao Sun, Xiu-Shen Wei
The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space.
no code implementations • 7 Feb 2023 • Xiu-Shen Wei, Xuhao Sun, Yang shen, Anqi Xu, Peng Wang, Faen Zhang
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.
Ranked #4 on Long-tail Learning on CIFAR-10-LT (ρ=10)
4 code implementations • The European Conference on Computer Vision (ECCV) 2022 • Hao Chen, Xiu-Shen Wei, Faen Zhang, Yang shen, Hui Xu, Liang Xiao
Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images).
4 code implementations • 28 Sep 2022 • Yang shen, Xuhao Sun, Xiu-Shen Wei, Qing-Yuan Jiang, Jian Yang
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks.
3 code implementations • 28 Sep 2022 • Xiu-Shen Wei, He-Yang Xu, Faen Zhang, Yuxin Peng, Wei Zhou
Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data.
3 code implementations • IEEE International Conference on Multimedia and Expo (ICME) 2022 • Yang shen, Xuhao Sun, Xiu-Shen Wei, Hanxu Hu, Zhipeng Chen
In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities.
3 code implementations • Proceedings of the AAAI Conference on Artificial Intelligence 2022 • Shu-Lin Xu, Faen Zhang, Xiu-Shen Wei, Jianhua Wang
Specifically, by producing attention guidance from deep activations of input images, our hard-attention is realized by keeping a few useful deep descriptors and forming them as a bag of multi-instance learning.
no code implementations • 1 Mar 2022 • Jiabao Wang, Yang Li, Xiu-Shen Wei, Hang Li, Zhuang Miao, Rui Zhang
Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID).
1 code implementation • IJCAI 2022 • Yu-Yan Xu, Yang shen, Xiu-Shen Wei, Jian Yang
The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e. g., different bird species)by utilizing freely available but noisy web data. As the label noises signifcantly hurt the network training, it is desirable to distinguish and eliminate noisy images.
2 code implementations • CVPR 2022 • Yin-Yin He, Peizhen Zhang, Xiu-Shen Wei, Xiangyu Zhang, Jian Sun
In this paper, we explore to excavate the confusion matrix, which carries the fine-grained misclassification details, to relieve the pairwise biases, generalizing the coarse one.
1 code implementation • NeurIPS 2021 • Xiu-Shen Wei, Yang shen, Xuhao Sun, Han-Jia Ye, Jian Yang
Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations.
no code implementations • 11 Nov 2021 • Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, Serge Belongie
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.
1 code implementation • ICCV 2021 • Zeren Sun, Yazhou Yao, Xiu-Shen Wei, Yongshun Zhang, Fumin Shen, Jianxin Wu, Jian Zhang, Heng-Tao Shen
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
1 code implementation • 15 Jun 2021 • Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan
To this end, we propose Learning to Decompose Network (LeadNet) to contextualize the meta-learned ``support-to-target'' strategy, leveraging the context of instances with one or mixed latent attributes in a support set.
2 code implementations • Association for the Advancement of Artificial Intelligence 2021 • Yongshun Zhang, Xiu-Shen Wei, Boyan Zhou, Jianxin Wu
In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on various complex paradigms (e. g., meta learning).
1 code implementation • ICCV 2021 • Yin-Yin He, Jianxin Wu, Xiu-Shen Wei
We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method.
Ranked #21 on Long-tail Learning on iNaturalist 2018
no code implementations • CVPR 2021 • Peng Wang, Kai Han, Xiu-Shen Wei, Lei Zhang, Lei Wang
Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases.
Ranked #10 on Long-tail Learning on CIFAR-10-LT (ρ=10)
1 code implementation • IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 • ZhaoMin Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Multi-Label Classification Multi-label Image Recognition with Partial Labels
no code implementations • 29 Dec 2020 • Xiu-Shen Wei, Yu-Yan Xu, Yazhou Yao, Jia Wei, Si Xi, Wenyuan Xu, Weidong Zhang, Xiaoxin Lv, Dengpan Fu, Qing Li, Baoying Chen, Haojie Guo, Taolue Xue, Haipeng Jing, Zhiheng Wang, Tianming Zhang, Mingwen Zhang
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
1 code implementation • 6 Aug 2020 • Zeren Sun, Xian-Sheng Hua, Yazhou Yao, Xiu-Shen Wei, Guosheng Hu, Jian Zhang
To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images.
no code implementations • ECCV 2020 • Zhao-Min Chen, Xin Jin, Borui Zhao, Xiu-Shen Wei, Yanwen Guo
To address this issue, we present a simple but effective Hierarchical Context Embedding (HCE) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of a series of region-based detectors by mining contextual cues.
no code implementations • ECCV 2020 • Quan Cui, Qing-Yuan Jiang, Xiu-Shen Wei, Wu-Jun Li, Osamu Yoshie
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
2 code implementations • 24 Jun 2020 • Wei Luo, Hengmin Zhang, Jun Li, Xiu-Shen Wei
We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter.
Ranked #15 on Fine-Grained Image Classification on Stanford Dogs
1 code implementation • 2 May 2020 • Benyi Hu, Ren-Jie Song, Xiu-Shen Wei, Yazhou Yao, Xian-Sheng Hua, Yuehu Liu
Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner.
1 code implementation • CVPR 2020 • Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, Xiu-Shen Wei
Specifically, by integrating an image-level multi-label classifier upon the detection backbone, we can obtain the sparse but crucial image regions corresponding to categorical information, thanks to the weakly localization ability of the classification manner.
1 code implementation • CVPR 2020 • Boyan Zhou, Quan Cui, Xiu-Shen Wei, Zhao-Min Chen
Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods.
Ranked #37 on Long-tail Learning on CIFAR-10-LT (ρ=10)
1 code implementation • 6 Jul 2019 • Xiu-Shen Wei, Jianxin Wu, Quan Cui
Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications.
2 code implementations • CVPR 2019 • Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Ranked #12 on Multi-Label Classification on PASCAL VOC 2007
no code implementations • 22 Jan 2019 • Xiu-Shen Wei, Quan Cui, Lei Yang, Peng Wang, Lingqiao Liu
The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products.
no code implementations • 11 Dec 2018 • Xiu-Shen Wei, Chen-Lin Zhang, Lingqiao Liu, Chunhua Shen, Jianxin Wu
Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification.
1 code implementation • 11 May 2018 • Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, Jianxin Wu
To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task.
no code implementations • 1 Nov 2017 • Yu Chen, Chunhua Shen, Hao Chen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang
In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity.
no code implementations • 20 Jul 2017 • Xiu-Shen Wei, Chen-Lin Zhang, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou
Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications.
Ranked #11 on Single-object discovery on COCO_20k
no code implementations • 8 May 2017 • Xiu-Shen Wei, Chen-Lin Zhang, Yao Li, Chen-Wei Xie, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou
Reusable model design becomes desirable with the rapid expansion of machine learning applications.
2 code implementations • ICCV 2017 • Yu Chen, Chunhua Shen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang
In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity.
Ranked #15 on Pose Estimation on MPII Human Pose
no code implementations • 23 May 2016 • Xiu-Shen Wei, Chen-Wei Xie, Jianxin Wu
Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations.
1 code implementation • 18 Apr 2016 • Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu, Zhi-Hua Zhou
Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.
no code implementations • 21 Apr 2015 • Bin-Bin Gao, Xiu-Shen Wei, Jianxin Wu, Weiyao Lin
In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system.
no code implementations • 20 Apr 2015 • Yu Zhang, Xiu-Shen Wei, Jianxin Wu, Jianfei Cai, Jiangbo Lu, Viet-Anh Nguyen, Minh N. Do
Most existing works heavily rely on object / part detectors to build the correspondence between object parts by using object or object part annotations inside training images.