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SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning

Paper · 2026 · Jingfan Yang, Rui Zhang, Liang Hong et al.

Summary

Multimodal models often lean on whichever modality is easiest to exploit, such as text, and leave other inputs like images underused. The authors call this modality laziness. They argue that earlier remedies, which reweight modalities or strengthen unimodal branches, still push weaker modalities toward semantics the dominant one already covers, so complementary information is lost. SCOPE (Shared-and-COmplementary cue PrEservation) tackles this in three stages:

  • Attention-based refinement plus a mutual information-guided objective separate shared content from modality-specific content. The objective rewards dependence within a sample and penalises dependence across samples.
  • Modalities are aligned by making their k-NN semantic graphs structurally consistent, which avoids pairing individual points. The graph construction is derived to be parameter-free.
  • A structure-aware diffusion attention fuses the cues without flattening them.

The method was evaluated against eight baselines on five benchmarks, including MVSA, TumEmo, CREMA-D and IEMOCAP. SCOPE outperformed every baseline, with accuracy gains reaching 27.10%. The work matters because it shows the gain comes from making non-dominant modalities useful in their own right, not from improving fused accuracy alone.

Key points

  • Laziness diagnostic on MVSA. With concatenation fusion, the visual-only branch reaches only 54.14%, while multimodal accuracy is 76.49%. SCOPE lifts visual-only accuracy to 76.88% and multimodal accuracy to 83.82%.
  • Ablation on CREMA-D. Removing attention-based feature refinement cuts accuracy from 84.14% to 70.16%. Dropping the MI-guided disentanglement lowers it to 80.38%.
  • Graph density trade-off on MVSA. Raising k from 10 to 60 improves accuracy from 80.47% to 84.78%, but per-epoch time grows from 12s to 95s. Most of the gain is reached by k = 30.
  • Over-smoothing. Diffusion attention keeps higher Dirichlet energy, meaning less over-smoothing, than GAT-style stacking. The default diffusion depth is T = 3.
  • Limitation. The method requires paired, aligned multimodal data. Handling unaligned or noisy pairs is left for future work.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Wuhan UniversityWuhan · China47 works · 1,246 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Vision & language · 811 works

Cite

Cite as Jingfan Yang, Rui Zhang, Liang Hong and Ke Yuan. 2026. SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1453

@inproceedings{yang2026scope,
  title = {{SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning}},
  author = {Jingfan Yang, Rui Zhang, Liang Hong and Ke Yuan},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1453},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1453}
}

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