In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

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 …

How to visually localize multiple sound sources in unconstrained videos is a formidable
problem, especially when lack of the pairwise sound-object annotations. To solve this …

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 …

There are rich synchronized audio and visual events in our daily life. Inside the events,
audio scenes are associated with the corresponding visual objects; meanwhile, sounding …

The joint analysis of audio and video is a powerful tool that can be applied to various
contexts, including action, speech, and sound recognition, audio-visual video parsing …

Recent progress in deep learning has enabled many advances in sound separation and
visual scene understanding. However, extracting sound sources which are apparent in …

We tackle the problem of learning object detectors without supervision. Differently from
weakly-supervised object detection, we do not assume image-level class labels. Instead, we …

Sound source localization in visual scenes aims to localize objects emitting the sound in a
given image. Recent works showing impressive localization performance typically rely on …