Search Results for author: Xiu-Shen Wei

Found 43 papers, 27 papers with code

An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing

no code implementations CVPR 2024 Feiran Hu, Chenlin Zhang, Jiangliang Guo, Xiu-Shen Wei, Lin Zhao, Anqi Xu, Lingyan Gao

In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects.

Contrastive Learning Self-Supervised Learning

Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval

1 code implementation21 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.

Attribute Deep Hashing +2

Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning

2 code implementations14 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.

Fine-Grained Image Recognition

Watch out Venomous Snake Species: A Solution to SnakeCLEF2023

1 code implementation19 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.

Data Augmentation

Equiangular Basis Vectors

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.

Metric Learning

Delving Deep into Simplicity Bias for Long-Tailed Image Recognition

no code implementations7 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.

Long-tail Learning Self-Supervised Learning

Automatic Check-Out via Prototype-based Classifier Learning from Single-Product Exemplars

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).


An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

3 code implementations28 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.

Few-Shot Learning

SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

4 code implementations28 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.

Image Retrieval Retrieval

A Channel Mix Method for Fine-Grained Cross-Modal Retrieval

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.

Cross-Modal Retrieval Retrieval

Dual Attention Networks for Few-Shot Fine-Grained Recognition

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.

Hard Attention Meta-Learning

Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification

no code implementations1 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).

Clustering Contrastive Learning +3

Webly-Supervised Fine-Grained Recognition with Partial Label Learning

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.

Partial Label Learning

Relieving Long-tailed Instance Segmentation via Pairwise Class Balance

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.

Instance Segmentation Semantic Segmentation

A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval

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.

Attribute Decoder +2

Fine-Grained Image Analysis with Deep Learning: A Survey

no code implementations11 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.

Fine-Grained Image Recognition Image Retrieval +1

Contextualizing Meta-Learning via Learning to Decompose

1 code implementation15 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.

Attribute Few-Shot Image Classification +1

Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks

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).

Data Augmentation Meta-Learning

Distilling Virtual Examples for Long-tailed Recognition

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.

Knowledge Distillation Long-tail Learning

Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

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.

Classification Contrastive Learning +4

Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

no code implementations29 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.

Salvage Reusable Samples from Noisy Data for Robust Learning

1 code implementation6 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.


ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval

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.

Image Retrieval Retrieval

Hierarchical Context Embedding for Region-based Object Detection

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.

Object object-detection +1

PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks

1 code implementation2 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.

Content-Based Image Retrieval Retrieval

Exploring Categorical Regularization for Domain Adaptive Object Detection

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.

Domain Adaptation Object +2

BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition

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.

Long-tail Learning Representation Learning

Deep Learning for Fine-Grained Image Analysis: A Survey

1 code implementation6 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.

Fine-Grained Image Recognition Image Generation +2

RPC: A Large-Scale Retail Product Checkout Dataset

no code implementations22 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.

Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification

no code implementations11 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.

Vehicle Re-Identification

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples

1 code implementation11 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.

Few-Shot Learning Fine-Grained Image Recognition

Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization

no code implementations1 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.

Pose Estimation

Deep Descriptor Transforming for Image Co-Localization

no code implementations8 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.

Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition

no code implementations23 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.

Fine-Grained Image Recognition

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

1 code implementation18 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.

Image Retrieval Object Proposal Generation +1

Deep Spatial Pyramid: The Devil is Once Again in the Details

no code implementations21 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.

General Classification Image Classification

Weakly Supervised Fine-Grained Image Categorization

no code implementations20 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.

Fine-Grained Image Classification Image Categorization +1

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