105 citations · 369 across the 26 of their papers we have counts for
26 papers
Holistic Prototype Attention Network for Few-Shot VOS
Yin Tang, Tao Chen, Xiruo Jiang +3
Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen classes by resorting to a small set of support images that contain pixel-level object annotatio…
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation
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. Howe…
Semi-Supervised Semantic Segmentation With Region Relevance
Rui Chen, Tao Chen, Qiong Wang +1
Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to gener…
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Gensheng Pei, Yazhou Yao, Fumin Shen +3
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often…
Attention Map Guided Transformer Pruning for Edge Device
Junzhu Mao, Yazhou Yao, Zeren Sun +3
Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identificatio…
FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
Huafeng Liu, Pai Peng, Tao Chen +3
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. The current correlation-…
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Gensheng Pei, Fumin Shen, Yazhou Yao +3
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS). Most of the previous methods directly extract and fuse the motion…
Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
Tao Chen, Yazhou Yao, Lei Zhang +3
Weakly supervised semantic segmentation with only image-level labels aims to reduce annotation costs for the segmentation task. Existing approaches generally leverage class activat…
Exploring Linear Feature Disentanglement For Neural Networks
Tiantian He, Zhibin Li, Yongshun Gong +3
Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs). Due to the complex non-linear characteristic of samples, the o…
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Zeren Sun, Yazhou Yao, Xiu-Shen Wei +5
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distingui…
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
Yazhou Yao, Tao Chen, Guosen Xie +5
Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels…
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Yazhou Yao, Zeren Sun, Chuanyi Zhang +4
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily…
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Tao Chen, Guosen Xie, Yazhou Yao +4
One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimi…
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Huafeng Liu, Chuanyi Zhang, Yazhou Yao +4
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images…
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
Xiu-Shen Wei, Yu-Yan Xu, Yazhou Yao +14
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda Uni…
Field-wise Learning for Multi-field Categorical Data
Zhibin Li, Jian Zhang, Yongshun Gong +2
We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect…
Data-driven Meta-set Based Fine-Grained Visual Classification
Chuanyi Zhang, Yazhou Yao, Xiangbo Shu +3
Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, lea…
Salvage Reusable Samples from Noisy Data for Robust Learning
Zeren Sun, Xian-Sheng Hua, Yazhou Yao +3
Due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images ten…
PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks
Benyi Hu, Ren-Jie Song, Xiu-Shen Wei +3
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 u…
Extracting Visual Knowledge from the Internet: Making Sense of Image Data
Yazhou Yao, Jian Zhang, Xiansheng Hua +2
Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data. Extensive…
Dynamically Visual Disambiguation of Keyword-based Image Search
Yazhou Yao, Zeren Sun, Fumin Shen +6
Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy.…
Deep Representation Learning for Road Detection through Siamese Network
Huafeng Liu, Xiaofeng Han, Xiangrui Li +3
Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information ac…
Road Segmentation with Image-LiDAR Data Fusion
Huafeng Liu, Yazhou Yao, Zeren Sun +3
Robust road segmentation is a key challenge in self-driving research. Though many image-based methods have been studied and high performances in dataset evaluations have been repor…
Towards Automatic Construction of Diverse, High-quality Image Dataset
Yazhou Yao, Jian Zhang, Fumin Shen +4
The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models develo…