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Touch100k:

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A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation

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+ + Ning Cheng1, + + + Changhao Guan1, + + + Jing Gao1, + + + Weihao Wang1, + + + You Li1, + + + Fandong Meng2, + + + Jie Zhou2, + + + Bin Fang3, + + + Jinan Xu1, + + + Wenjuan Han1 + +
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+ + 1 Beijing Jiaotong University   + + + 2 WeChat AI, Tencent Inc.   + + + 3 Beijing University of Posts and Telecommunications + +
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Abstract

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+ Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. + Despite its significance, current tactile research mainly focuses on visual and tactile modalities, overlooking the language domain. + Inspired by this insight, we construct Touch100k, a paired touch-language-vision dataset at the scale of 100k, featuring tactile sensation descriptions + in multiple granularities (i.e., sentence-level natural expressions with rich semantics, including contextual and dynamic relationships, + and phrase-level descriptions capturing the key features of tactile sensations). Based on the dataset, we propose a pre-training method, + Touch-Language-Vision Representation Learning through Curriculum Linking (TLV-Link, for short), inspired by the concept of curriculum learning. TLV-Link aims + to learn a tactile representation for the GelSight sensor and capture the relationship between tactile, language, and visual modalities. + We evaluate our model's performance across two task categories (namely, material property identification and robot grasping prediction), + focusing on tactile representation and zero-shot touch understanding. The experimental results demonstrate that TLV-Link achieves significant advancements, + establishing a new state-of-the-art performance in touch-centric multimodal representation learning. Additionally, the results validate the efficacy of the constructed Touch100k dataset. +

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+ Credit: The design of this project page references the project pages of + Nerfies. +

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