Audio-visual learning, aimed at exploiting the relationship between audio and visual
modalities, has drawn considerable attention since deep learning started to be used …

Despite surveillance systems becoming increasingly ubiquitous in our living environment,
automated surveillance, currently based on video sensory modality and machine …

Our objective is to transform a video into a set of discrete audio-visual objects using self-
supervised learning. To this end, we introduce a model that uses attention to localize and …

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 …

There is a natural correlation between the visual and auditive elements of a video. In this
work we leverage this connection to learn general and effective models for both audio and …

We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos,
learns to locate image regions which produce sounds and separate the input sounds into a …

Recent deep learning approaches have achieved impressive performance on visual sound
separation tasks. However, these approaches are mostly built on appearance and optical …

Visual events are usually accompanied by sounds in our daily lives. We pose the question:
Can the machine learn the correspondence between visual scene and the sound, and …

Sounds originate from object motions and vibrations of surrounding air. Inspired by the fact
that humans is capable of interpreting sound sources from how objects move visually, we …

Discriminatively localizing sounding objects in cocktail-party, ie, mixed sound scenes, is
commonplace for humans, but still challenging for machines. In this paper, we propose a two …