348 citations · 699 across the 7 of their papers we have counts for
10 papers
Billion-scale semi-supervised learning for image classification
I. Zeki Yalniz, Hervé Jégou, Kan Chen +2
This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collec…
Large Scale Holistic Video Understanding
Ali Diba, Mohsen Fayyaz, Vivek Sharma +4
Video recognition has been advanced in recent years by benchmarks with rich annotations. However, research is still mainly limited to human action or sports recognition - focusing…
Exploring the Challenges towards Lifelong Fact Learning
Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi +3
So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups. Here, we introduce a new large-scale alternative. What makes the propos…
Exploring the Limits of Weakly Supervised Pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan +5
State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet,…
Large-Scale Visual Relationship Understanding
Ji Zhang, Yannis Kalantidis, Marcus Rohrbach +3
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples. In real-worl…
ConvNet Architecture Search for Spatiotemporal Feature Learning
Du Tran, Jamie Ray, Zheng Shou +2
Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation,…