6 citations · 14 across the 5 of their papers we have counts for
5 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…
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…
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…
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.…
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…