Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify
vision-language tasks such as image/video captioning and question answering. While …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

The canonical approach to video captioning dictates a caption generation model to learn
from offline-extracted dense video features. These feature extractors usually operate on …

Video clip retrieval and captioning tasks play an essential role in multimodal research and
are the fundamental research problem for multimodal understanding and generation. The …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

Abstract Knowledge distillation becomes a de facto standard to improve the performance of
small neural networks. Most of the previous works propose to regress the representational …