Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Contrastive Learning has recently received interest due to its success in self-supervised
representation learning in the computer vision domain. However, the origins of Contrastive …

We propose Make-A-Video--an approach for directly translating the tremendous recent
progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple …

Autonomous agents have made great strides in specialist domains like Atari games and Go.
However, they typically learn tabula rasa in isolated environments with limited and manually …

Contrastive language-image pretraining has shown great success in learning visual-textual
joint representation from web-scale data, demonstrating remarkable “zero-shot” …

Capitalizing on large pre-trained models for various downstream tasks of interest have
recently emerged with promising performance. Due to the ever-growing model size, the …

Video recognition has been dominated by the end-to-end learning paradigm–first initializing
a video recognition model with weights of a pretrained image model and then conducting …

This paper presents OmniVL, a new foundation model to support both image-language and
video-language tasks using one universal architecture. It adopts a unified transformer-based …

Video understanding requires reasoning at multiple spatiotemporal resolutions--from short
fine-grained motions to events taking place over longer durations. Although transformer …

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot
video and text understanding, without using any labels on downstream tasks. VideoCLIP …