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Computer Science > Computer Vision and Pattern Recognition

arXiv:2604.11579 (cs)
[Submitted on 13 Apr 2026]

Title:Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

Authors:Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu, Joon Son Chung, Arda Senocak
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Abstract:We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The challenge is amplified by existing datasets, which predominantly contain close-up, low-diversity images. We propose a model that learns local visuo-tactile alignment via dense cross-modal feature interactions, producing tactile saliency maps for touch-conditioned material segmentation. To overcome dataset constraints, we introduce: (i) in-the-wild multi-material scene images that expand visual diversity, and (ii) a material-diversity pairing strategy that aligns each tactile sample with visually varied yet tactilely consistent images, improving contextual localization and robustness to weak signals. We also construct two new tactile-grounded material segmentation datasets for quantitative evaluation. Experiments on both new and existing benchmarks show that our approach substantially outperforms prior visuo-tactile methods in tactile localization.
Comments: CVPR 2026. Project page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2604.11579 [cs.CV]
  (or arXiv:2604.11579v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2604.11579
arXiv-issued DOI via DataCite

Submission history

From: Arda Senocak [view email]
[v1] Mon, 13 Apr 2026 14:57:52 UTC (31,451 KB)
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