Abstract Convolutional Neural Networks (CNNs) have been the dominant model for video
action recognition. Due to the huge memory and compute demand, popular action …

Current state-of-the-art models for video action recognition are mostly based on expensive
3D ConvNets. This results in a need for large GPU clusters to train and evaluate such …

In recent years, a number of approaches based on 2D or 3D convolutional neural networks
(CNN) have emerged for video action recognition, achieving state-of-the-art results on …

We have seen great progress in video action recognition in recent years. There are several
models based on convolutional neural network (CNN) and some recent transformer based …

Deep learning models have achieved excellent recognition results on large-scale video
benchmarks. However, they perform poorly when applied to videos with rare scenes or …

Short-range motion features and long-range dependencies are two complementary and vital
cues for action recognition in videos, but it remains unclear how to efficiently and effectively …

Deep 3D CNNs for video action recognition are designed to learn powerful representations
in the joint spatio-temporal feature space. In practice however, because of the large number …

We propose a self-supervised learning method to jointly reason about spatial and temporal
context for video recognition. Recent self-supervised approaches have used spatial context …

In classic video action recognition, labels may not contain enough information about the
diverse video appearance and dynamics, thus, existing models that are trained under the …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …