Similarity has always been a key aspect in computer science and statistics. Any time two
element vectors are compared, many different similarity approaches can be used …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

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 …

Humans view the world through many sensory channels, eg, the long-wavelength light
channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right …

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 …

Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …

Self-supervised learning aims to learn representations from the data itself without explicit
manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning-the …

Video anomaly detection under weak labels is formulated as a typical multiple-instance
learning problem in previous works. In this paper, we provide a new perspective, ie, a …

We present a self-supervised learning approach to learn audio-visual representations from
video and audio. Our method uses contrastive learning for cross-modal discrimination of …

We investigate methods for combining multiple self-supervised tasks---ie, supervised tasks
where data can be collected without manual labeling---in order to train a single visual …