SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models

• URL: http://arxiv.org/abs/2210.03794v1
• Date: Fri, 7 Oct 2022 19:35:08 GMT
• Title: SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models
• Authors: Omiros Pantazis, Gabriel Brostow, Kate Jones, Oisin Mac Aodha
• Abstract summary: Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs. Due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. We present a new approach called SVL-Adapter that combines the complementary strengths of both vision-language pretraining and self-supervised representation learning.
• Score: 9.017387427570538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot image classification performance. However, due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. To combat this, a series of light-weight adaptation methods have been proposed to efficiently adapt such models when limited supervision is available. In this work, we show that while effective on internet-style datasets, even those remedies under-deliver on classification tasks with images that differ significantly from those commonly found online. To address this issue, we present a new approach called SVL-Adapter that combines the complementary strengths of both vision-language pretraining and self-supervised representation learning. We report an average classification accuracy improvement of 10% in the low-shot setting when compared to existing methods, on a set of challenging visual classification tasks. Further, we present a fully automatic way of selecting an important blending hyperparameter for our model that does not require any held-out labeled validation data. Code for our project is available here: https://github.com/omipan/svl_adapter.

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