In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

The desire to reduce the dependence on curated, labeled datasets and to leverage the vast
quantities of unlabeled data has triggered renewed interest in unsupervised (or self …

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 …

Violence detection has been studied in computer vision for years. However, previous work
are either superficial, eg, classification of short-clips, and the single scenario, or …

In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to
answer questions regarding different visual objects, sounds, and their associations in …

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 …

We focus on multi-modal fusion for egocentric action recognition, and propose a novel
architecture for multi-modal temporal-binding, ie the combination of modalities within a …

The thud of a bouncing ball, the onset of speech as lips open--when visual and audio events
occur together, it suggests that there might be a common, underlying event that produced …