# SITTA: A Semantic Image-Text Alignment for Image Captioning

**Authors:** Fabian Paischer, Thomas Adler, Markus Hofmarcher, Sepp Hochreiter  
**arXiv:** [2307.05591](https://arxiv.org/abs/2307.05591)  
**Date:** 2023-07-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.05591v1/

## Key points

- The paper addresses the importance of textual and semantic comprehension in generating accurate image captions.
- Comprehension involves object detection, modeling relationships between objects, assessing scene semantics, and representing knowledge in a language space.
- Pretrained language models are conditioned on pretrained multi-modal models to enhance language capabilities and image-language mappings.
- Two novel methods are proposed for constructing a linear mapping to transfer semantics between embedding spaces of pretrained models.
- The first method aligns the embedding space of the multi-modal language encoder with that of the pretrained LM using token correspondences.
- The second method leverages additional data consisting of image-text pairs to construct a mapping from vision to language space.
- By utilizing these semantic mappings, image captioning becomes accessible for LMs without gradient information.
- Strong captioning performance is achieved on popular datasets such as MS-COCO and Flickr30k using different sources of data.
- Even with limited data, their method outperforms other zero-shot and finetuned competitors.
- Ablation studies demonstrate that even LMs with only 250M parameters can generate decent captions when employing these semantic mappings.
- This approach makes image captioning more feasible for institutions with restricted computational resources.

## Summary

The paper titled "SITTA: A Semantic Image-Text Alignment for Image Captioning" addresses the importance of textual and semantic comprehension in generating accurate image captions. This comprehension involves object detection, modeling of relationships between objects, assessing the semantics of the scene, and representing the extracted knowledge in a language space. To enhance language capabilities and ensure effective image-language mappings, pretrained language models (LMs) are conditioned on pretrained multi-modal models that can process both images and text. However, it is unclear how to transfer the semantics detected by the vision encoder of the multi-modal model to the LM effectively. The authors propose two novel methods for constructing a linear mapping that successfully transfers semantics between the embedding spaces of these pretrained models. The first method aligns the embedding space of the multi-modal language encoder with that of the pretrained LM using token correspondences. The second method leverages additional data consisting of image-text pairs to directly construct a mapping from vision to language space. By utilizing these semantic mappings, image captioning becomes accessible for LMs without access to gradient information. The authors achieve strong captioning performance on popular datasets such as MS-COCO and Flickr30k by using different sources of data. Even with limited data, their method outperforms other zero-shot and finetuned competitors. Ablation studies demonstrate that even LMs with only 250M parameters can generate decent captions when employing these semantic mappings. This approach makes image captioning more feasible for institutions with restricted computational resources. Overall, this paper introduces innovative techniques for aligning visual and linguistic representations in order to improve image caption generation.