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Vision models' metonymy undermines attention-based interpretability, study finds

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv introduces the concept of "visual metonymy" in vision models, where parts of an object encode information about the whole object. This phenomenon undermines the interpretability of attention-based methods that assume locality, meaning a part should only encode information about its corresponding image region. The study demonstrates that modern vision transformers violate this assumption, rendering part-based reasoning and interpretability techniques unreliable. AI

IMPACT Highlights a fundamental issue in vision model interpretability, potentially requiring new approaches for understanding model behavior.

RANK_REASON The cluster contains a research paper detailing a new finding about vision model interpretability.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Vision models' metonymy undermines attention-based interpretability, study finds

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ananthu Aniraj, Cassio F. Dantas, Dino Ienco, Massimiliano Mancini, Diego Marcos ·

    Metonymy in vision models undermines attention-based interpretability

    arXiv:2605.06095v1 Announce Type: new Abstract: Part-based reasoning is a classical strategy to make a computer vision model directly focus on the object parts that are relevant to the downstream task. In the context of deep learning, this also serves to improve by-design interpr…

  2. arXiv cs.CV TIER_1 English(EN) · Diego Marcos ·

    Metonymy in vision models undermines attention-based interpretability

    Part-based reasoning is a classical strategy to make a computer vision model directly focus on the object parts that are relevant to the downstream task. In the context of deep learning, this also serves to improve by-design interpretability, often by using part-centric attention…

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