Class Incremental Learning with Pre-trained Vision-Language Models

• URL: http://arxiv.org/abs/2310.20348v1
• Date: Tue, 31 Oct 2023 10:45:03 GMT
• Title: Class Incremental Learning with Pre-trained Vision-Language Models
• Authors: Xialei Liu, Xusheng Cao, Haori Lu, Jia-wen Xiao, Andrew D. Bagdanov, Ming-Ming Cheng
• Abstract summary: We propose an approach to exploiting pre-trained vision-language models (e.g. CLIP) that enables further adaptation. Experiments on several conventional benchmarks consistently show a significant margin of improvement over the current state-of-the-art.
• Score: 59.15538370859431
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advent of large-scale pre-trained models, interest in adapting and exploiting them for continual learning scenarios has grown. In this paper, we propose an approach to exploiting pre-trained vision-language models (e.g. CLIP) that enables further adaptation instead of only using zero-shot learning of new tasks. We augment a pre-trained CLIP model with additional layers after the Image Encoder or before the Text Encoder. We investigate three different strategies: a Linear Adapter, a Self-attention Adapter, each operating on the image embedding, and Prompt Tuning which instead modifies prompts input to the CLIP text encoder. We also propose a method for parameter retention in the adapter layers that uses a measure of parameter importance to better maintain stability and plasticity during incremental learning. Our experiments demonstrate that the simplest solution -- a single Linear Adapter layer with parameter retention -- produces the best results. Experiments on several conventional benchmarks consistently show a significant margin of improvement over the current state-of-the-art.

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