The deployment of video action recognition models on mobile and embedded devices is
challenging due to the limited computational resources and storage capacity. To address …

Temporal modeling has attracted the attention of a large number of researchers in the past
few years. In this work, we propose a new video architecture, termed as Temporal Excitation …

Human action recognition is a well-established task in the field of computer vision. However,
accurately representing spatio-temporal information remains a challenge due to the complex …

This paper introduces a new video understanding network referred to as' ViViP'(Video Vision
Poolformer), which leverages poolformer and transformer techniques. It begins by encoding …

Conventionally, spatiotemporal modeling network and its complexity are the two most
concentrated research topics in video action recognition. Existing state-of-the-art methods …

The ability to recognize actions throughout a video is essential for surveillance, self-driving,
and many other applications. Although many researchers have investigated deep neural …

Two-stream convolutional neural networks show great promise for action recognition tasks.
However, most two-stream based approaches train the appearance and motion …

We present a general and flexible video-level framework for learning action models in
videos. This method, called temporal segment network (TSN), aims to model long-range …

In recent years, deep convolutional neural networks (DCNN) have been widely used in the
field of video action recognition. Attention mechanisms are also increasingly utilized in …

The two-stream convolutional network has been proved to be one milestone in the study of
video-based action recognition. Lots of recent works modify internal structure of two-stream …