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Efficient data-driven behavior identification based on vision transformers for human activity understanding
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AI
DOI：10.1016/j.neucom.2023.01.067.png)
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Abstract
En 中文
With the development of computer vision, the research on human activity understanding has been greatly promoted. The recognition algorithm based on vision transformer has made some achievements in a large number of computer vision tasks, but it still needs to be driven by a large amount of data. How to get rid of the constraints of large amounts of data is crucial for human behavior recognition based on vision transformer. This paper focuses on solving the dilemma of big data, and tries to achieve a high-performance model through a small amount of high information human activity data. The advantage of our work is that by studying feature distribution, we proposed a core weight entropy data information evaluation method for obtaining high information data, and through redundant information elimination strategy, we can avoid introducing similar data. A large number of experimental results show the effec-tiveness of the proposed method. Compared with existing methods, our method reduces the data con-sumption by 5% to 30%, and can achieve the performance of using only 50% of 100% data. More importantly, the data our method selected has no redundancy, which is not available in other methods. In addition, we carried out a large number of ablation experiments to prove the rationality of the method. The work of this paper solves the challenge of relying on a large amount of data when using the visual converter to recognize human behavior, which is of practical significance for realizing efficient human activity understanding research with low data.CO 2023 Elsevier B.V. All rights reserved.
Keywords:
Human activity understanding
Vision transformers
Data problems
Computer vision
Deep learning
Active learning
Journal
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6.5W