Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

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 …

The ability to quickly learn a new task with minimal instruction-known as few-shot learning-is
a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot …

We present pure-transformer based models for video classification, drawing upon the recent
success of such models in image classification. Our model extracts spatio-temporal tokens …

We present a framework for learning multimodal representations from unlabeled data using
convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer …

Abstract We propose Anticipative Video Transformer (AVT), an end-to-end attention-based
video modeling architecture that attends to the previously observed video in order to …

Abstract We introduce the Action Transformer model for recognizing and localizing human
actions in video clips. We repurpose a Transformer-style architecture to aggregate features …

Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Learning to capture long-range relations is fundamental to image/video recognition. Existing
CNN models generally rely on increasing depth to model such relations which is highly …