#5837. Incremental human action recognition with dual memory

August 2026publication date
Proposal available till 16-06-2025
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Journal’s subject area:
Computer Vision and Pattern Recognition;
Signal Processing;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Incremental learning is a topic of great interest in the current state of machine learning research. Real-world problems often require a classifier to incorporate new knowledge while preserving what was learned before. One of the most challenging problems in computer vision is Human Action Recognition (HAR) in videos. However, most of the existing works approach HAR from a non-incremental point of view. This work proposes a framework for performing HAR in the incremental learning scenario called Incremental Human Action Recognition with Dual Memory (IHAR-DM). IHAR-DM contains three main components: a 3D convolutional neural network for capturing Spatio-temporal features; a Triplet Network to perform metric learning; and the dual-memory Extreme Value Machine, which is introduced in this work. The proposed method is compared with 10 other state-of-the-art incremental learning models. We propose five experimental settings containing different numbers of tasks and classes using two widely known HAR datasets: UCF-101 and HMDB51.
Keywords:
Dual-memory Extreme Value Machine; Human Action Recognition; Incremental learning; Metric Learning; Triplet Networks

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