Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime

• URL: http://arxiv.org/abs/2305.02297v1
• Date: Wed, 3 May 2023 17:42:54 GMT
• Title: Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime
• Authors: Chuhan Zhang, Antoine Miech, Jiajun Shen, Jean-Baptiste Alayrac, Pauline Luc
• Abstract summary: We look into task adaptation in the low-data regime, and provide a study of the existing adaptation methods for generative Visual Language Models. We show important benefits of self-labelling, i.e. using the model's own predictions to self-improve when having access to a larger number of unlabelled images.
• Score: 23.255873641249263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks. While humans are known to efficiently learn new tasks from a few examples, deep learning models struggle with adaptation from few examples. In this work, we look into task adaptation in the low-data regime, and provide a thorough study of the existing adaptation methods for generative Visual Language Models. And we show important benefits of self-labelling, i.e. using the model's own predictions to self-improve when having access to a larger number of unlabelled images of the same distribution. Our study demonstrates significant gains using our proposed task adaptation pipeline across a wide range of visual language tasks such as visual classification (ImageNet), visual captioning (COCO), detailed visual captioning (Localised Narratives) and visual question answering (VQAv2).

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