Fine-grained Image Captioning with CLIP Reward

Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit Bansal


Abstract
Modern image captioning models are usually trained with text similarity objectives. However, since reference captions in public datasets often describe the most salient common objects, models trained with the text similarity objectives tend to ignore specific and detailed aspects of an image that distinguish it from others. Towards more descriptive and distinctive caption generation, we propose to use CLIP, a multimodal encoder trained on huge image-text pairs from the web, to calculate multi-modal similarity and use it as a reward function. We also propose a simple finetuning strategy of CLIP text encoder to improve grammar that does not require extra text annotation. This completely eliminates the need for reference captions during the reward computation. To comprehensively evaluate descriptive captions, we introduce FineCapEval, a new dataset for caption evaluation with fine-grained criteria: overall, background, object, relations. In our experiments on text-to-image retrieval and FineCapEval, the proposed CLIP-guided model generates more distinctive captions than the CIDEroptimized model. We also show that our unsupervised grammar finetuning of the CLIP text encoder alleviates the degeneration problem of the naive CLIP reward. Lastly, we show human analysis where the annotators strongly prefer CLIP reward to CIDEr and MLE objectives on diverse criteria.
Anthology ID:
2022.findings-naacl.39
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
517–527
Language:
URL:
https://aclanthology.org/2022.findings-naacl.39
DOI:
10.18653/v1/2022.findings-naacl.39
Bibkey:
Cite (ACL):
Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, and Mohit Bansal. 2022. Fine-grained Image Captioning with CLIP Reward. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 517–527, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Fine-grained Image Captioning with CLIP Reward (Cho et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.39.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.39.mp4
Code
 j-min/clip-caption-reward
Data
COCO