2 citations · 5 across the 3 of their papers we have counts for
7 papers · 1 filter
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…
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Xiaopeng Yan, Ziliang Chen, Anni Xu +3
Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived fro…
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
Keze Wang, Liang Lin, Xiaopeng Yan +3
Though quite challenging, leveraging large-scale unlabeled or partially labeled data in learning systems (e.g., model/classifier training) has attracted increasing attentions due t…