Contrastive Learning has recently received interest due to its success in self-supervised
representation learning in the computer vision domain. However, the origins of Contrastive …

The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …

In video transformers, the time dimension is often treated in the same way as the two spatial
dimensions. However, in a scene where objects or the camera may move, a physical point …

We propose to explore a new problem called audio-visual segmentation (AVS), in which the
goal is to output a pixel-level map of the object (s) that produce sound at the time of the …

Vision transformers (ViTs) have achieved impressive results on various computer vision
tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained …

Localizing objects in image collections without supervision can help to avoid expensive
annotation campaigns. We propose a simple approach to this problem, that leverages the …

The objective of this work is to localize sound sources that are visible in a video without
using manual annotations. Our key technical contribution is to show that, by training the …

Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or
more speakers. The successful ASD depends on accurate interpretation of short-term and …