Linear Alignment of Vision-language Models for Image Captioning

• URL: http://arxiv.org/abs/2307.05591v3
• Date: Tue, 6 Feb 2024 09:33:48 GMT
• Title: Linear Alignment of Vision-language Models for Image Captioning
• Authors: Fabian Paischer, Markus Hofmarcher, Sepp Hochreiter, Thomas Adler
• Abstract summary: We propose a more efficient training protocol that fits a linear mapping between image and text embeddings of CLIP. This bypasses the need for gradient computation and results in a lightweight captioning method called ReCap. We evaluate ReCap on MS-COCO, Flickr30k, VizWiz, and MSRVTT.
• Score: 9.746397419479447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, vision-language models like CLIP have advanced the state of the art in a variety of multi-modal tasks including image captioning and caption evaluation. Many approaches adapt CLIP-style models to a downstream task by training a mapping network between CLIP and a language model. This is costly as it usually involves calculating gradients for large models. We propose a more efficient training protocol that fits a linear mapping between image and text embeddings of CLIP via a closed-form solution. This bypasses the need for gradient computation and results in a lightweight captioning method called ReCap, which can be trained up to 1000 times faster than existing lightweight methods. Moreover, we propose two new learning-based image-captioning metrics that build on CLIP score along with our linear mapping. Furthermore, we combine ReCap with our new metrics to design an iterative datastore-augmentation loop (DAL) based on synthetic captions. We evaluate ReCap on MS-COCO, Flickr30k, VizWiz, and MSRVTT. ReCap achieves performance comparable to state-of-the-art lightweight methods on established metrics while outperforming them on our new metrics, which are better aligned with human ratings on Flickr8k-Expert and Flickr8k-Crowdflower. Finally, we demonstrate that ReCap transfers well to other domains and that our DAL leads to a performance boost.

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