Search Results for author: Xiu-Shen Wei

Found 31 papers, 11 papers with code

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).

Contrastive Learning Domain Adaptation +2

Relieving Long-tailed Instance Segmentation via Pairwise Class Balance

1 code implementation8 Jan 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

no code implementations 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.

Image Retrieval

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

Contextualizing Multiple Tasks via Learning to Decompose

no code implementations15 Jun 2021 Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts.

Few-Shot Image Classification Meta-Learning

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

1 code implementation 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

no code implementations 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

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 Detection

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

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 Detection

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.

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 +1

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

no code implementations11 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

no code implementations18 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

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

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