Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models

Sivan Doveh, Assaf Arbelle, Sivan Harary, Paola Cascante-Bonilla, Amit Alfassy, Roei Herzig, Donghyun Kim, Raja Giryes, Rogerio Feris, Rameswar Panda, Shimon Ullman, Leonid Karlinsky

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, visual question answering, captioning, and more. However, the aligned image-text spaces learned by all the popular VL models are still suffering from the so-called 'object bias' - their representations behave as 'bags of nouns', mostly ignoring or downsizing the attributes, relations, and states of objects described/appearing in texts/images. Although some great attempts at fixing these 'compositional reasoning' issues were proposed in the recent literature, the problem is still far from being solved. In this paper, we uncover two factors limiting the VL models' compositional reasoning performance. These two factors are properties of the paired VL dataset used for finetuning and pre-training the VL model: (i) the caption quality, or in other words 'image-alignment', of the texts; and (ii) the 'density' of the captions in the sense of mentioning all the details appearing on the image. We propose a fine-tuning approach for automatically treating these factors leveraging a standard VL dataset (CC3M). Applied to CLIP, we demonstrate its significant compositional reasoning performance increase of up to ∼ 27% over the base model, up to ∼ 20% over the strongest baseline, and by 6.7% on average.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
StatePublished - 2023
Event37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States
Duration: 10 Dec 202316 Dec 2023


FundersFunder number
Institute for Information and Communications Technology Promotion
Institute of Information and Communications Technology Planning and Evaluation
Artificial Intelligence Graduate School Program, Korea University
Ministry of Science, ICT and Future Planning2019-0-00079
Ministry of Science, ICT and Future Planning


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