Investigating Self-Supervised Methods for Label-Efficient Learning

• URL: http://arxiv.org/abs/2406.17460v1
• Date: Tue, 25 Jun 2024 10:56:03 GMT
• Title: Investigating Self-Supervised Methods for Label-Efficient Learning
• Authors: Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng, Josef Kittler, Muhammad Awais,
• Abstract summary: We study different self supervised pretext tasks, namely contrastive learning, clustering, and masked image modelling for their low-shot capabilities. We introduce a framework involving both mask image modelling and clustering as pretext tasks, which performs better across all low-shot downstream tasks. When testing the model on full scale datasets, we show performance gains in multi-class classification, multi-label classification and semantic segmentation.
• Score: 27.029542823306866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision transformers combined with self-supervised learning have enabled the development of models which scale across large datasets for several downstream tasks like classification, segmentation and detection. The low-shot learning capability of these models, across several low-shot downstream tasks, has been largely under explored. We perform a system level study of different self supervised pretext tasks, namely contrastive learning, clustering, and masked image modelling for their low-shot capabilities by comparing the pretrained models. In addition we also study the effects of collapse avoidance methods, namely centring, ME-MAX, sinkhorn, on these downstream tasks. Based on our detailed analysis, we introduce a framework involving both mask image modelling and clustering as pretext tasks, which performs better across all low-shot downstream tasks, including multi-class classification, multi-label classification and semantic segmentation. Furthermore, when testing the model on full scale datasets, we show performance gains in multi-class classification, multi-label classification and semantic segmentation.

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