Search Results for author: Yazhou Yao

Found 37 papers, 21 papers with code

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 +2

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

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 +2

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

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

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

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.

Object Segmentation +4

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

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.

Memorization

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

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.

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.

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

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

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

no code implementations 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 Memorization

PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction

no code implementations CVPR 2022 Zeren Sun, Fumin Shen, Dan Huang, Qiong Wang, Xiangbo Shu, Yazhou Yao, Jinhui Tang

Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.

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).

Disentanglement

Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation

1 code implementation20 Jun 2022 Tao Chen, Yazhou Yao, Lei Zhang, Qiong Wang, Guo-Sen Xie, Fumin Shen

Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.

Object Pseudo Label +4

FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network

1 code implementation19 Jan 2023 Huafeng Liu, Pai Peng, Tao Chen, Qiong Wang, Yazhou Yao, Xian-Sheng Hua

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images.

Few-Shot Semantic Segmentation

Attention Map Guided Transformer Pruning for Edge Device

1 code implementation4 Apr 2023 Junzhu Mao, Yazhou Yao, Zeren Sun, Xingguo Huang, Fumin Shen, Heng-Tao Shen

Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity.

Person Re-Identification

Co-attention Propagation Network for Zero-Shot Video Object Segmentation

1 code implementation8 Apr 2023 Gensheng Pei, Yazhou Yao, Fumin Shen, Dan Huang, Xingguo Huang, Heng-Tao Shen

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects.

Optical Flow Estimation Semantic Segmentation +3

Semi-Supervised Semantic Segmentation With Region Relevance

1 code implementation23 Apr 2023 Rui Chen, Tao Chen, Qiong Wang, Yazhou Yao

The most common approach is to generate pseudo-labels for unlabeled images to augment the training data.

Pseudo Label Pseudo Label Filtering +2

Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation

1 code implementation9 May 2023 Tao Chen, Yazhou Yao, Jinhui Tang

Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts.

Denoising Image Generation +2

Holistic Prototype Attention Network for Few-Shot VOS

1 code implementation16 Jul 2023 Yin Tang, Tao Chen, Xiruo Jiang, Yazhou Yao, Guo-Sen Xie, Heng-Tao Shen

Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames.

Graph Attention Semantic Segmentation +2

Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning

no code implementations15 Dec 2023 Mengmeng Sheng, Zeren Sun, Zhenhuang Cai, Tao Chen, Yichao Zhou, Yazhou Yao

There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks.

Contrastive Learning Meta-Learning +1

Hierarchical Graph Pattern Understanding for Zero-Shot VOS

1 code implementation15 Dec 2023 Gensheng Pei, Fumin Shen, Yazhou Yao, Tao Chen, Xian-Sheng Hua, Heng-Tao Shen

However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene.

Optical Flow Estimation Semantic Segmentation +4

Spatial Structure Constraints for Weakly Supervised Semantic Segmentation

1 code implementation20 Jan 2024 Tao Chen, Yazhou Yao, Xingguo Huang, Zechao Li, Liqiang Nie, Jinhui Tang

In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion.

Object Object Localization +2

Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection

no code implementations17 Feb 2024 Huafeng Liu, Mengmeng Sheng, Zeren Sun, Yazhou Yao, Xian-Sheng Hua, Heng-Tao Shen

Specifically, we propose Class-Balance-based sample Selection (CBS) to prevent the tail class samples from being neglected during training.

Learning with noisy labels

VideoMAC: Video Masked Autoencoders Meet ConvNets

1 code implementation29 Feb 2024 Gensheng Pei, Tao Chen, Xiruo Jiang, Huafeng Liu, Zeren Sun, Yazhou Yao

In this paper, we propose a new approach termed as \textbf{VideoMAC}, which combines video masked autoencoders with resource-friendly ConvNets.

Pose Tracking Representation Learning +4

Poly Kernel Inception Network for Remote Sensing Detection

1 code implementation10 Mar 2024 Xinhao Cai, Qiuxia Lai, Yuwei Wang, Wenguan Wang, Zeren Sun, Yazhou Yao

Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context.

Object object-detection +1

Group Benefits Instances Selection for Data Purification

no code implementations23 Mar 2024 Zhenhuang Cai, Chuanyi Zhang, Dan Huang, Yuanbo Chen, Xiuyun Guan, Yazhou Yao

Comprehensive experimental results on synthetic and real-world datasets demonstrate the superiority of GRIP over the existing state-of-the-art methods.

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 Relation +1

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