@inproceedings{kim-etal-2026-mllmclip,
title = "{MLLMCLIP}: Feature-Level Distillation of {MLLM} for Robust Vision-Language Representations",
author = "Kim, Jongsuk and
Wu, Qiyu and
Mao, Zhuoyuan and
Wakaki, Hiromi and
Kim, Junmo and
Mitsufuji, Yuki",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1187/",
pages = "27208--27222",
ISBN = "979-8-89176-459-0",
abstract = "Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent studies mitigate this issue by augmenting training with synthetic hard negatives generated by a cascade of large language models and text-to-image models, which incurs substantial pipeline overhead. We instead propose MLLMCLIP, a heterogeneous distillation framework that transfers multimodal knowledge directly from a generative Multimodal Large Language Model (MLLM) teacher into a discriminative CLIP student, bypassing synthetic data entirely. To bridge the architectural mismatch between the two paradigms, we introduce an attention-based per-layer token selection and a CKA-based distillation loss. Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability."
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%0 Conference Proceedings
%T MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
%A Kim, Jongsuk
%A Wu, Qiyu
%A Mao, Zhuoyuan
%A Wakaki, Hiromi
%A Kim, Junmo
%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 kim-etal-2026-mllmclip
%X Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent studies mitigate this issue by augmenting training with synthetic hard negatives generated by a cascade of large language models and text-to-image models, which incurs substantial pipeline overhead. We instead propose MLLMCLIP, a heterogeneous distillation framework that transfers multimodal knowledge directly from a generative Multimodal Large Language Model (MLLM) teacher into a discriminative CLIP student, bypassing synthetic data entirely. To bridge the architectural mismatch between the two paradigms, we introduce an attention-based per-layer token selection and a CKA-based distillation loss. Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
%U https://aclanthology.org/2026.emnlp-main.1187/
%P 27208-27222
Markdown (Informal)
[MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations](https://aclanthology.org/2026.emnlp-main.1187/) (Kim et al., EMNLP 2026)
ACL