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    <abstract>Multimodal retrieval, which seeks to retrieve relevant content across modalities such as text or image, supports applications from AI search to contents production. Despite the success of separate-encoder approaches like CLIP aligning modality-specific embeddings with contrastive learning, recent multimodal large language models (MLLMs) enable a unified encoder that directly processes composed inputs. While flexible and advanced, we identify that unified encoders trained with conventional contrastive learning are prone to learn modality shortcut, leading to poor robustness under distribution shifts. We propose a modality composition awareness framework to mitigate this issue. Concretely, it consists of a preference loss enforces multimodal embeddings to outperform their unimodal counterparts, and a composition regularization objective aligns multimodal embeddings with prototypes composed from its unimodal parts. These objectives explicitly model structural relationships between the composed representation and its unimodal counterparts. Experiments on various benchmarks show gains in out-of-distribution retrieval, highlighting modality composition awareness as a effective principle for robust composed multimodal retrieval when utilizing MLLMs as the unified encoder.</abstract>
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%0 Conference Proceedings
%T MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval
%A Wu, Qiyu
%A Cui, Shuyang
%A Hayakawa, Satoshi
%A Wang, Wei-Yao
%A Wakaki, Hiromi
%A Mitsufuji, Yuki
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-mca
%X Multimodal retrieval, which seeks to retrieve relevant content across modalities such as text or image, supports applications from AI search to contents production. Despite the success of separate-encoder approaches like CLIP aligning modality-specific embeddings with contrastive learning, recent multimodal large language models (MLLMs) enable a unified encoder that directly processes composed inputs. While flexible and advanced, we identify that unified encoders trained with conventional contrastive learning are prone to learn modality shortcut, leading to poor robustness under distribution shifts. We propose a modality composition awareness framework to mitigate this issue. Concretely, it consists of a preference loss enforces multimodal embeddings to outperform their unimodal counterparts, and a composition regularization objective aligns multimodal embeddings with prototypes composed from its unimodal parts. These objectives explicitly model structural relationships between the composed representation and its unimodal counterparts. Experiments on various benchmarks show gains in out-of-distribution retrieval, highlighting modality composition awareness as a effective principle for robust composed multimodal retrieval when utilizing MLLMs as the unified encoder.
%U https://aclanthology.org/2026.emnlp-main.246/
%P 5612-5628

Markdown (Informal)

[MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval](https://aclanthology.org/2026.emnlp-main.246/) (Wu et al., EMNLP 2026)

• MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval (Wu et al., EMNLP 2026)

ACL

• Qiyu Wu, Shuyang Cui, Satoshi Hayakawa, Wei-Yao Wang, Hiromi Wakaki, and Yuki Mitsufuji. 2026. MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5612–5628, Budapest, Hungary. Association for Computational Linguistics.