2 citations · 3 across the 10 of their papers we have counts for
7 papers · 1 filter
Contrastive Touch-to-Touch Pretraining
Samanta Rodriguez, Yiming Dou, William van den Bogert +4
Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unifie…
Tactile Functasets: Neural Implicit Representations of Tactile Datasets
Sikai Li, Samanta Rodriguez, Yiming Dou +2
Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sens…
Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation
Samanta Rodriguez, Yiming Dou, Miquel Oller +2
Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specifi…
Efficient Vision-Language Pre-training by Cluster Masking
Zihao Wei, Zixuan Pan, Andrew Owens
We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed.…
Tactile-Augmented Radiance Fields
Yiming Dou, Fengyu Yang, Yi Liu +2
We present a scene representation, which we call a tactile-augmented radiance field (TaRF), that brings vision and touch into a shared 3D space. This representation can be used to…
Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark
Ziyang Chen, Israel D. Gebru, Christian Richardt +4
We present a new dataset called Real Acoustic Fields (RAF) that captures real acoustic room data from multiple modalities. The dataset includes high-quality and densely captured ro…
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
Fengyu Yang, Chao Feng, Ziyang Chen +8
The ability to associate touch with other modalities has huge implications for humans and computational systems. However, multimodal learning with touch remains challenging due to…