Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …

With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …

The goal of this work is to recognise phrases and sentences being spoken by a talking face,
with or without the audio. Unlike previous works that have focussed on recognising a limited …

We present a joint audio-visual model for isolating a single speech signal from a mixture of
sounds such as other speakers and background noise. Solving this task using only audio as …

Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to
compensate for their inherent limitations. However, existing MML methods often optimize a …

In this paper, we introduce a novel problem of audio-visual event localization in
unconstrained videos. We define an audio-visual event as an event that is both visible and …

The goal of this work is to recognise phrases and sentences being spoken by a talking face,
with or without the audio. Unlike previous works that have focussed on recognising a limited …

Recent years have witnessed the proliferation of offensive content online such as fake news,
propaganda, misinformation, and disinformation. While initially this was mostly about textual …

Several end-to-end deep learning approaches have been recently presented which extract
either audio or visual features from the input images or audio signals and perform speech …