Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Abstract 3D point clouds are a crucial type of data collected by LiDAR sensors and widely
used in transportation applications due to its concise descriptions and accurate localization …

The objective of this paper is visual-only self-supervised video representation learning. We
make the following contributions:(i) we investigate the benefit of adding semantic-class …

We present a large-scale study on unsupervised spatiotemporal representation learning
from videos. With a unified perspective on four recent image-based frameworks, we study a …

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-
agnostic joint representations of image content and natural language. We extend the …

Abstract We propose Anticipative Video Transformer (AVT), an end-to-end attention-based
video modeling architecture that attends to the previously observed video in order to …

Human observers can learn to recognize new categories of images from a handful of
examples, yet doing so with artificial ones remains an open challenge. We hypothesize that …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

This paper proposes a simple self-supervised approach for learning a representation for
visual correspondence from raw video. We cast correspondence as prediction of links in a …

Abstract Machine learning, particularly deep learning has boosted medical image analysis
over the past years. Training a good model based on deep learning requires large amount …