Self-Supervised Image Captioning with CLIP
- URL: http://arxiv.org/abs/2306.15111v2
- Date: Thu, 2 Nov 2023 17:57:54 GMT
- Title: Self-Supervised Image Captioning with CLIP
- Authors: Chuanyang Jin
- Abstract summary: We introduce a self-supervised image captioning method.
After learning an initial signal from a small labeled dataset, our method transitions to self-supervised learning on unlabeled data.
Despite utilizing less than 2% of the labeled COCO dataset, our method delivers a performance comparable to state-of-the-art models trained on the complete dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Image captioning, a fundamental task in vision-language understanding, seeks
to generate accurate natural language descriptions for provided images. Current
image captioning approaches heavily rely on high-quality image-caption pairs,
which can be hard to obtain for many domains. To address this, we introduce a
self-supervised image captioning method. After learning an initial signal from
a small labeled dataset, our method transitions to self-supervised learning on
unlabeled data, leveraging the auxiliary task of enhancing the CLIP relevance
between images and generated captions. Remarkably, despite utilizing less than
2% of the labeled COCO dataset, our method delivers a performance comparable to
state-of-the-art models trained on the complete dataset. Human evaluations
further reveal that our method produces captions with greater distinctiveness
and informativeness, two attributes inherently challenging to achieve through
supervised learning.
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