@inproceedings{yang-etal-2026-scope,
title = "{SCOPE}: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning",
author = "Yang, Jingfan and
Zhang, Rui and
Hong, Liang and
Yuan, Ke",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1453/",
pages = "29066--29078",
ISBN = "979-8-89176-395-1",
abstract = "Multimodal learning aims to learn unified multimodal representations from heterogeneous modalities and supports many natural language processing tasks. However, multimodal models often exhibit modality laziness: over-relying on a dominant modality and under-exploiting complementary signals. Existing approaches typically strengthen unimodal training or rebalance modality contributions, but they may still emphasize shared semantics and overlook modality-specific cues. To address this, we propose SCOPE, a unified framework for learning complete multimodal representations, achieving Shared-and-COmplementary cue PrEservation. Firstly, SCOPE uses a mutual information-guided disentanglement module to separate shared semantics from modality-specific cues and mitigate representation collapse. Secondly, SCOPE aligns modalities by enforcing structural consistency between modality-wise semantic graphs, avoiding brittle point-wise matching. Finally, SCOPE performs balanced fusion via structure-aware diffusion attention to integrate shared and complementary cues without feature homogenization. Experiments on four benchmark datasets show that SCOPE consistently outperforms SOTA baselines, achieving up to 27.10{\%} accuracy improvement."
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<abstract>Multimodal learning aims to learn unified multimodal representations from heterogeneous modalities and supports many natural language processing tasks. However, multimodal models often exhibit modality laziness: over-relying on a dominant modality and under-exploiting complementary signals. Existing approaches typically strengthen unimodal training or rebalance modality contributions, but they may still emphasize shared semantics and overlook modality-specific cues. To address this, we propose SCOPE, a unified framework for learning complete multimodal representations, achieving Shared-and-COmplementary cue PrEservation. Firstly, SCOPE uses a mutual information-guided disentanglement module to separate shared semantics from modality-specific cues and mitigate representation collapse. Secondly, SCOPE aligns modalities by enforcing structural consistency between modality-wise semantic graphs, avoiding brittle point-wise matching. Finally, SCOPE performs balanced fusion via structure-aware diffusion attention to integrate shared and complementary cues without feature homogenization. Experiments on four benchmark datasets show that SCOPE consistently outperforms SOTA baselines, achieving up to 27.10% accuracy improvement.</abstract>
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%0 Conference Proceedings
%T SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning
%A Yang, Jingfan
%A Zhang, Rui
%A Hong, Liang
%A Yuan, Ke
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-scope
%X Multimodal learning aims to learn unified multimodal representations from heterogeneous modalities and supports many natural language processing tasks. However, multimodal models often exhibit modality laziness: over-relying on a dominant modality and under-exploiting complementary signals. Existing approaches typically strengthen unimodal training or rebalance modality contributions, but they may still emphasize shared semantics and overlook modality-specific cues. To address this, we propose SCOPE, a unified framework for learning complete multimodal representations, achieving Shared-and-COmplementary cue PrEservation. Firstly, SCOPE uses a mutual information-guided disentanglement module to separate shared semantics from modality-specific cues and mitigate representation collapse. Secondly, SCOPE aligns modalities by enforcing structural consistency between modality-wise semantic graphs, avoiding brittle point-wise matching. Finally, SCOPE performs balanced fusion via structure-aware diffusion attention to integrate shared and complementary cues without feature homogenization. Experiments on four benchmark datasets show that SCOPE consistently outperforms SOTA baselines, achieving up to 27.10% accuracy improvement.
%U https://aclanthology.org/2026.findings-acl.1453/
%P 29066-29078
Markdown (Informal)
[SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning](https://aclanthology.org/2026.findings-acl.1453/) (Yang et al., Findings 2026)
ACL