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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation">
<meta name="keywords" content="Touch-Centric Multimodal">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Touch100k</title>
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<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Touch100k:</h1>
<h1 class="title is-2 publication-title">A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation</h1>
<div class="is-size-5 publication-authors">
<br>
<span class="author-block">
<a style="font-weight:normal;">Ning Cheng<sup>1</sup></a>,
</span>
<span class="author-block">
<a style="font-weight:normal;">Changhao Guan<sup>1</sup></a>,
</span>
<span class="author-block">
<a style="font-weight:normal;">Jing Gao<sup>1</sup></a>,
</span>
<span class="author-block">
<a style="font-weight:normal;">Weihao Wang<sup>1</sup></a>,
</span>
<span class="author-block">
<a style="font-weight:normal;">You Li<sup>1</sup></a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=sA8U4S0AAAAJ" style="font-weight:normal;">Fandong Meng<sup>2</sup></a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=S1tit3AAAAAJ" style="font-weight:normal;">Jie Zhou<sup>2</sup></a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=5G47IcIAAAAJ&hl=zh-CN" style="font-weight:normal;">Bin Fang<sup>3</sup></a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=wMuW0W4AAAAJ&hl=en" style="font-weight:normal;">Jinan Xu<sup>1</sup></a>,
</span>
<span class="author-block">
<a href="https://winniehan.github.io/" style="font-weight:normal;">Wenjuan Han<sup>1</sup></a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<sup>1 </sup>Beijing Jiaotong University
</span>
<span class="author-block">
<sup>2 </sup> WeChat AI, Tencent Inc.
</span>
<span class="author-block">
<sup>3 </sup> Beijing University of Posts and Telecommunications
</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://drive.google.com/drive/folders/1QOvbkIZtpJpz4Ry_Zg3ouXX-zLJNqu9m?usp=sharing" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Dataset</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/cocacola-lab/TLV-Link" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" style="background-color:#efeff081" id="Abstract">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
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., <em>sentence-level</em> natural expressions with rich semantics, including contextual and dynamic relationships,
and <em>phrase-level</em> 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.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="credit">
<p>
Credit: The design of this project page references the project pages of
<a href="https://github.com/nerfies/nerfies.github.io" class="credit-link">Nerfies</a>.
</p>
</section>
</body>
</html>