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 …

To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …

The canonical approach to video action recognition dictates a neural model to do a classic
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …

The vision community is witnessing a modeling shift from CNNs to Transformers, where pure
Transformer architectures have attained top accuracy on the major video recognition …

Sign languages are visual languages using manual articulations and non-manual elements
to convey information. For sign language recognition and translation, the majority of existing …

Human activity recognition (HAR) is one of the most important and challenging problems in
the computer vision. It has critical application in wide variety of tasks including gaming …

Abstract Spatial-temporal, channel-wise, and motion patterns are three complementary and
crucial types of information for video action recognition. Conventional 2D CNNs are …

Abstract We introduce Video Transformer (VidTr) with separable-attention for video
classification. Comparing with commonly used 3D networks, VidTr is able to aggregate …

On public benchmarks, current action recognition techniques have achieved great success.
However, when used in real-world applications, eg sport analysis, which requires the …

In a real-world scenario, human actions are typically out of the distribution from training data,
which requires a model to both recognize the known actions and reject the unknown …