811 citations · 4.2k across the 47 of their papers we have counts for
10 papers · 1 filter
Prototypical Contrastive Learning of Unsupervised Representations
Junnan Li, Pan Zhou, Caiming Xiong +1
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive…
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Isabela Albuquerque, Nikhil Naik, Junnan Li +2
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained us…
Towards Noise-resistant Object Detection with Noisy Annotations
Junnan Li, Caiming Xiong, Richard Socher +1
Training deep object detectors requires significant amount of human-annotated images with accurate object labels and bounding box coordinates, which are extremely expensive to acqu…
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Junnan Li, Richard Socher, Steven C. H. Hoi
Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent direction…
Learning from Noisy Anchors for One-stage Object Detection
Hengduo Li, Zuxuan Wu, Chen Zhu +3
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their inte…
WSLLN: Weakly Supervised Natural Language Localization Networks
Mingfei Gao, Larry S. Davis, Richard Socher +1
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries. To learn the correspondence between visual se…