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New OTTER probe decodes visual grounding in frozen MLLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called OTTER to probe frozen Multimodal Large Language Models (MLLMs) and decode grounding information from modifier tokens. This technique uses Optimal Transport to align generated tokens with specific visual regions, creating grounding maps. The study found that instance-discriminative visual information, particularly for spatial terms, can be decoded from these modifiers, and this grounding remains informative even when the context is perturbed or across different datasets. AI

IMPACT This research could improve how MLLMs understand and refer to specific objects and attributes within visual scenes.

RANK_REASON The cluster contains a research paper detailing a new method for analyzing MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New OTTER probe decodes visual grounding in frozen MLLMs

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The cluster contains a research paper detailing a new method for analyzing MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Barbara Toniella Corradini (AI for Good), Caterina Gallegati (University of Siena, Italy), Ludovica Genovese (AI for Good), Vittorio Murino (AI for Good) ·

    From What to Which: Decoding Modifier Grounding in Frozen MLLMs

    arXiv:2610.12305v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) can describe increasingly complex visual scenes, token-level grounding becomes crucial. Yet, when an MLLM generates "the yellow banana on the left", established grounding approaches focus …

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