2 citations · 5 across the 4 of their papers we have counts for
6 papers
SEPT: Towards Scalable and Efficient Visual Pre-Training
Yiqi Lin, Huabin Zheng, Huaping Zhong +4
Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing t…
Semantically Coherent Out-of-Distribution Detection
Jingkang Yang, Haoqi Wang, Litong Feng +4
Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortuna…
Progressive Representative Labeling for Deep Semi-Supervised Learning
Xiaopeng Yan, Riquan Chen, Litong Feng +3
Deep semi-supervised learning (SSL) has experienced significant attention in recent years, to leverage a huge amount of unlabeled data to improve the performance of deep learning w…
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph
Jingkang Yang, Weirong Chen, Litong Feng +3
Webly supervised learning becomes attractive recently for its efficiency in data expansion without expensive human labeling. However, adopting search queries or hashtags as web lab…
Webly Supervised Image Classification with Self-Contained Confidence
Jingkang Yang, Litong Feng, Weirong Chen +4
This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and directly using search queries as web labels. Although WSL…
Chinese/English mixed Character Segmentation as Semantic Segmentation
Huabin Zheng, Jingyu Wang, Zhengjie Huang +2
OCR character segmentation for multilingual printed documents is difficult due to the diversity of different linguistic characters. Previous approaches mainly focus on monolingual…