Deep convolutional networks are widely used in video action recognition. 3D convolutions
are one prominent approach to deal with the additional time dimension. While 3D …

Deep learning approaches have been established as the main methodology for video
classification and recognition. Recently, 3-dimensional convolutions have been used to …

To interpret deep neural networks, one main approach is to dissect the visual input and find
the prototypical parts responsible for the classification. However, existing methods often …

Image Classification and Video Action Recognition are perhaps the two most foundational
tasks in computer vision. Consequently, explaining the inner workings of trained deep …

A small subset of explainability techniques developed initially for image recognition models
has recently been applied for interpretability of 3D Convolutional Neural Network models in …

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 …

Self-attention learns pairwise interactions to model long-range dependencies, yielding great
improvements for video action recognition. In this paper, we seek a deeper understanding of …

Convolutional neural networks (CNNs) have shown remarkable performance on various
tasks. Despite its widespread adoption, the decision procedure of the network still lacks …

To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-
art methods integrate 1D temporal convolutional filters into 2D CNN backbones. However …

Abstract 3D convolutional neural network (3D CNN) captures spatial and temporal
information on 3D data such as video sequences. However, due to the convolution and …