A Literature Review Study on Knowledge Distillation for Large Models of Image Segmentation

Dongkai Qi and Lim Chia Sien

European Journal of AI, Computing & Informatics, 2025, vol. 1, issue 1, 25-32

Abstract: With the rapid development of deep learning technology, image segmentation macromodels have achieved remarkable results in many fields. However, these large models often face problems such as high consumption of computational resources and difficulties in deployment. Knowledge distillation, as an effective model compression and optimisation technique, has gradually received attention from researchers. This paper provides a systematic review of the literature related to knowledge distillation for large models of image segmentation, and analyses the current status, advantages and shortcomings of the application of knowledge distillation in large models of image segmentation in terms of improving the AR interactive experience, solving the contradiction between real-time and accuracy, promoting the lightweight and efficient deployment of the model, enhancing the generalization capability of the model, and facilitating the fusion of multimodal data, etc. It also looks forward to the future research direction, aiming to provide a better solution for the research in the related fields. outlook, aiming to provide reference and inspiration for researchers in related fields.

Keywords: image segmentation; knowledge distillation; large models; model compression; AR interaction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:ejacia:v:1:y:2025:i:1:p:25-32

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