17 citations · 24 across the 6 of their papers we have counts for
6 papers
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…
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…
Refining Image Categorization by Exploiting Web Images and General Corpus
Yazhou Yao, Jian Zhang, Fumin Shen +3
Studies show that refining real-world categories into semantic subcategories contributes to better image modeling and classification. Previous image sub-categorization work relying…