Towards Comprehensive Multimodal Perception: Introducing the Touch-Language-Vision Dataset
<|reference_start|>Towards Comprehensive Multimodal Perception: Introducing the Touch-Language-Vision Dataset: Tactility provides crucial support and enhancement for the perception and interaction capabilities of both humans and robots. Nevertheless, the multimodal research related to touch primarily focuses on visual and tactile modalities, with limited exploration in the domain of language. Beyond vocabulary, sentence-level descriptions contain richer semantics. Based on this, we construct a touch-language-vision dataset named TLV (Touch-Language-Vision) by human-machine cascade collaboration, featuring sentence-level descriptions for multimode alignment. The new dataset is used to fine-tune our proposed lightweight training framework, STLV-Align (Synergistic Touch-Language-Vision Alignment), achieving effective semantic alignment with minimal parameter adjustments (1%). Project Page: https://xiaoen0.github.io/touch.page/.<|reference_end|>
@article{cheng2024towards,
    title={Towards Comprehensive Multimodal Perception: Introducing the
  Touch-Language-Vision Dataset},
    author={Ning Cheng, You Li, Jing Gao, Bin Fang, Jinan Xu, Wenjuan Han},
    journal={arXiv preprint arXiv:2403.09813},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09813},
    primaryClass={cs.CV cs.RO}
}