Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …

Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …

Despite having promising results, style transfer, which requires preparing style images in
advance, may result in lack of creativity and accessibility. Following human instruction, on …

Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Self-supervised representation learning is a critical problem in computer vision, as it
provides a way to pretrain feature extractors on large unlabeled datasets that can be used …

Weakly-supervised audio-visual violence detection aims to distinguish snippets containing
multimodal violence events with video-level labels. Many prior works perform audio-visual …

The visual world naturally exhibits a long-tailed distribution of open classes, which poses
great challenges to modern visual systems. Existing approaches either perform class re …

Large-scale pre-trained multimodal transformers, such as ViLBERT and UNITER, have
propelled the state of the art in vision-and-language (V+ L) research to a new level. Although …

Molecular representation learning plays an essential role in cheminformatics. Recently,
language model-based approaches have gained popularity as an alternative to traditional …

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware
dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human …