# ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition

arXiv: https://arxiv.org/abs/1802.07490 · HTML: https://mlsanity.com/paper/1802.07490/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 27 of 27)

- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models](https://mlsanity.com/paper/2301.06267/) (2023)
- [Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2401.12024/) (2024)
- [DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction](https://mlsanity.com/paper/2201.01367/) (2022)
- [Visuo-Haptic Object Perception for Robots: An Overview](https://mlsanity.com/paper/2203.11544/) (2022)
- [FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback](https://mlsanity.com/paper/2303.07997/) (2023)
- [Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning](https://mlsanity.com/paper/2204.00117/) (2022)
- [M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation](https://mlsanity.com/paper/2401.17032/) (2024)
- [ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations](https://mlsanity.com/paper/2506.20757/) (2025)
- [Learning Intuitive Physics with Multimodal Generative Models](https://mlsanity.com/paper/2101.04454/) (2021)
- [LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping](https://mlsanity.com/paper/2403.04934/) (2024)
- [Vision-Guided Active Tactile Perception for Crack Detection and Reconstruction](https://mlsanity.com/paper/2105.06325/) (2021)
- [Tactile MNIST: Benchmarking Active Tactile Perception](https://mlsanity.com/paper/2506.06361/) (2025)
- [ViTacGen: Robotic Pushing with Vision-to-Touch Generation](https://mlsanity.com/paper/2510.14117/) (2025)
- [Multimodal perception for dexterous manipulation](https://mlsanity.com/paper/2112.14298/) (2021)
- [Transferring Tactile Data Across Sensors](https://mlsanity.com/paper/2410.14310/) (2024)
- [ACROSS: A Deformation-Based Cross-Modal Representation for Robotic Tactile Perception](https://mlsanity.com/paper/2411.08533/) (2024)
- [ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers](https://mlsanity.com/paper/2505.20032/) (2025)
- [Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms](https://mlsanity.com/paper/2605.17336/) (2026)
- [Coincident Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2301.11368/) (2023)
- [VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification](https://mlsanity.com/paper/2209.08516/) (2022)
- [MLLM-Fabric: Multimodal Large Language Model-Driven Robotic Framework for Fabric Sorting and Selection](https://mlsanity.com/paper/2507.04351/) (2025)
- [MultiDiffSense: Diffusion-Based Multi-Modal Visuo-Tactile Image Generation Conditioned on Object Shape and Contact Pose](https://mlsanity.com/paper/2602.19348/) (2026)
- [Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch](https://mlsanity.com/paper/2604.09100/) (2026)
- [More with LESS -- Local Scene Representations for Tactile Imaging](https://mlsanity.com/paper/2606.14344/) (2026)
- [Cohort Organized Learning: Clustering Through Agreement](https://mlsanity.com/paper/2606.21743/) (2026)
- [VTLoc: Learning-based Tactile Contact Localization in Visual Point Clouds](https://mlsanity.com/paper/2607.16146/) (2026)

## Abstract (arXiv)

Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data.