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        <title>RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training</title>
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        <namePart type="given">Jinlin</namePart>
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    <abstract>Tactile perception is essential for human-environment interaction, and deriving tactile descriptions from multimodal data is a key challenge for embodied intelligence to understand human perception. Conventional approaches relying on extensive parameter learning for multimodal perception are rigid and computationally inefficient. To address this, we introduce Retrieval-Augmented Voting (RAV), a parameter-free method that constructs visual-tactile cross-modal knowledge directly. RAV retrieves similar visual-tactile data for given visual and tactile inputs and generates tactile descriptions through a voting mechanism. In experiments, we applied three voting strategies, SyncVote, DualVote and WeightVote, achieving performance comparable to large-scale cross-modal models without training. Comparative experiments across datasets of varying quality—defined by annotation accuracy and data diversity—demonstrate that RAV’s performance improves with higher-quality data at no additional computational cost. Code, and model checkpoints are opensourced at https://github.com/PluteW/RAV.</abstract>
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%0 Conference Proceedings
%T RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training
%A Wang, Jinlin
%A Ji, Yulong
%A Yang, Hongyu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-rav
%X Tactile perception is essential for human-environment interaction, and deriving tactile descriptions from multimodal data is a key challenge for embodied intelligence to understand human perception. Conventional approaches relying on extensive parameter learning for multimodal perception are rigid and computationally inefficient. To address this, we introduce Retrieval-Augmented Voting (RAV), a parameter-free method that constructs visual-tactile cross-modal knowledge directly. RAV retrieves similar visual-tactile data for given visual and tactile inputs and generates tactile descriptions through a voting mechanism. In experiments, we applied three voting strategies, SyncVote, DualVote and WeightVote, achieving performance comparable to large-scale cross-modal models without training. Comparative experiments across datasets of varying quality—defined by annotation accuracy and data diversity—demonstrate that RAV’s performance improves with higher-quality data at no additional computational cost. Code, and model checkpoints are opensourced at https://github.com/PluteW/RAV.
%U https://aclanthology.org/2025.emnlp-main.315/
%P 6198-6205

Markdown (Informal)

[RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training](https://aclanthology.org/2025.emnlp-main.315/) (Wang et al., EMNLP 2025)

• RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training (Wang et al., EMNLP 2025)

ACL

• Jinlin Wang, Yulong Ji, and Hongyu Yang. 2025. RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6198–6205, Suzhou, China. Association for Computational Linguistics.