Search Results for author: Yazhou Yao

Found 21 papers, 10 papers with code

Region Graph Embedding Network for Zero-Shot Learning

no code implementations ECCV 2020 Guo-Sen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao

To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.

Graph Embedding Zero-Shot Learning

Exploring Linear Feature Disentanglement For Neural Networks

no code implementations22 Mar 2022 Tiantian He, Zhibin Li, Yongshun Gong, Yazhou Yao, Xiushan Nie, Yilong Yin

Non-linear activation functions, e. g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs).


Jo-SRC: A Contrastive Approach for Combating Noisy Labels

1 code implementation CVPR 2021 Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, Zhenmin Tang

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.

Contrastive Learning

Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

1 code implementation23 Jan 2021 Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiushen Wei, Fumin Shen, Jian Zhang, Zhenmin Tang

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.

Fine-Grained Visual Recognition

Few-Shot Semantic Segmentation With Cyclic Memory Network

no code implementations ICCV 2021 Guo-Sen Xie, Huan Xiong, Jie Liu, Yazhou Yao, Ling Shao

Specifically, we first generate N pairs (key and value) of multi-resolution query features guided by the support feature and its mask.

Few-Shot Semantic Segmentation Semantic Segmentation

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.

Field-wise Learning for Multi-field Categorical Data

1 code implementation NeurIPS 2020 Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu

We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters.

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.

Data-driven Meta-set Based Fine-Grained Visual Classification

1 code implementation6 Aug 2020 Chuanyi Zhang, Yazhou Yao, Xiangbo Shu, Zechao Li, Zhenmin Tang, Qi Wu

To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.

Classification Fine-Grained Image Classification +3

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

Motion-Attentive Transition for Zero-Shot Video Object Segmentation

1 code implementation9 Mar 2020 Tianfei Zhou, Shunzhou Wang, Yi Zhou, Yazhou Yao, Jianwu Li, Ling Shao

In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation.

Ranked #6 on Unsupervised Video Object Segmentation on DAVIS 2016 (using extra training data)

Semantic Segmentation Unsupervised Video Object Segmentation +2

Extracting Visual Knowledge from the Internet: Making Sense of Image Data

no code implementations7 Jun 2019 Yazhou Yao, Jian Zhang, Xian-Sheng Hua, Fumin Shen, Zhenmin Tang

Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data.

Representation Learning

Dynamically Visual Disambiguation of Keyword-based Image Search

no code implementations27 May 2019 Yazhou Yao, Zeren Sun, Fumin Shen, Li Liu, Li-Min Wang, Fan Zhu, Lizhong Ding, Gangshan Wu, Ling Shao

To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.

General Classification Image Retrieval

Deep Representation Learning for Road Detection through Siamese Network

no code implementations26 May 2019 Huafeng Liu, Xiaofeng Han, Xiangrui Li, Yazhou Yao, Pu Huang, Zhenming Tang

We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network.

Autonomous Driving Representation Learning

Towards Automatic Construction of Diverse, High-quality Image Dataset

no code implementations22 Aug 2017 Yazhou Yao, Jian Zhang, Fumin Shen, Li Liu, Fan Zhu, Dongxiang Zhang, Heng-Tao Shen

To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.

Image Classification Object Detection

Refining Image Categorization by Exploiting Web Images and General Corpus

no code implementations16 Mar 2017 Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Wankou Yang, Zhenmin Tang

To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories.

Image Categorization

Exploiting Web Images for Dataset Construction: A Domain Robust Approach

no code implementations22 Nov 2016 Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Jingsong Xu, Zhenmin Tang

To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.

Domain Adaptation Image Classification +1

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