Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

• URL: http://arxiv.org/abs/2111.14447v1
• Date: Mon, 29 Nov 2021 11:01:49 GMT
• Title: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
• Authors: Yoad Tewel, Yoav Shalev, Idan Schwartz, Lior Wolf
• Abstract summary: Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. In this work, we repurpose such models to generate a descriptive text given an image at inference time. The resulting captions are much less restrictive than those obtained by supervised captioning methods.
• Score: 72.60554897161948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image. In this work, we repurpose such models to generate a descriptive text given an image at inference time, without any further training or tuning step. This is done by combining the visual-semantic model with a large language model, benefiting from the knowledge in both web-scale models. The resulting captions are much less restrictive than those obtained by supervised captioning methods. Moreover, as a zero-shot learning method, it is extremely flexible and we demonstrate its ability to perform image arithmetic in which the inputs can be either images or text and the output is a sentence. This enables novel high-level vision capabilities such as comparing two images or solving visual analogy tests.

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