CLUE: Crossmodal disambiguation via Language-vision Understanding with attEntion
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914315932532736 |
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| author | Abrini, Mouad Chetouani, Mohamed |
| author_facet | Abrini, Mouad Chetouani, Mohamed |
| contents | With the increasing integration of robots into daily life, human-robot interaction has become more complex and multifaceted. A critical component of this interaction is Interactive Visual Grounding (IVG), through which robots must interpret human intentions and resolve ambiguity. Existing IVG models generally lack a mechanism to determine when to ask clarification questions, as they implicitly rely on their learned representations. CLUE addresses this gap by converting the VLM's cross-modal attention into an explicit, spatially grounded signal for deciding when to ask. We extract text to image attention maps and pass them to a lightweight CNN to detect referential ambiguity, while a LoRA fine-tuned decoder conducts the dialog and emits grounding location tokens. We train on a real-world interactive dataset for IVG, and a mixed ambiguity set for the detector. With InViG-only supervision, our model surpasses a state-of-the-art method while using parameter-efficient fine-tuning. Similarly, the ambiguity detector outperforms prior baselines. Overall, CLUE turns the internal cross-modal attention of a VLM into an explicit, spatially grounded signal for deciding when to ask. The data and code are publicly available at: mouadabrini.github.io/clue |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08999 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CLUE: Crossmodal disambiguation via Language-vision Understanding with attEntion Abrini, Mouad Chetouani, Mohamed Robotics With the increasing integration of robots into daily life, human-robot interaction has become more complex and multifaceted. A critical component of this interaction is Interactive Visual Grounding (IVG), through which robots must interpret human intentions and resolve ambiguity. Existing IVG models generally lack a mechanism to determine when to ask clarification questions, as they implicitly rely on their learned representations. CLUE addresses this gap by converting the VLM's cross-modal attention into an explicit, spatially grounded signal for deciding when to ask. We extract text to image attention maps and pass them to a lightweight CNN to detect referential ambiguity, while a LoRA fine-tuned decoder conducts the dialog and emits grounding location tokens. We train on a real-world interactive dataset for IVG, and a mixed ambiguity set for the detector. With InViG-only supervision, our model surpasses a state-of-the-art method while using parameter-efficient fine-tuning. Similarly, the ambiguity detector outperforms prior baselines. Overall, CLUE turns the internal cross-modal attention of a VLM into an explicit, spatially grounded signal for deciding when to ask. The data and code are publicly available at: mouadabrini.github.io/clue |
| title | CLUE: Crossmodal disambiguation via Language-vision Understanding with attEntion |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.08999 |