CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment

• URL: http://arxiv.org/abs/2203.07190v1
• Date: Mon, 14 Mar 2022 15:29:27 GMT
• Title: CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment
• Authors: Haoyu Song, Li Dong, Wei-Nan Zhang, Ting Liu, Furu Wei
• Abstract summary: We show that CLIP can be a strong vision-language few-shot learner by leveraging the power of language. We propose a parameter-efficient fine-tuning strategy to boost the few-shot performance on the vqa task.
• Score: 102.17010696898113
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks. Previously, CLIP is only regarded as a powerful visual encoder. However, after being pre-trained by language supervision from a large amount of image-caption pairs, CLIP itself should also have acquired some few-shot abilities for vision-language tasks. In this work, we empirically show that CLIP can be a strong vision-language few-shot learner by leveraging the power of language. We first evaluate CLIP's zero-shot performance on a typical visual question answering task and demonstrate a zero-shot cross-modality transfer capability of CLIP on the visual entailment task. Then we propose a parameter-efficient fine-tuning strategy to boost the few-shot performance on the vqa task. We achieve competitive zero/few-shot results on the visual question answering and visual entailment tasks without introducing any additional pre-training procedure.

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