Summary of Transformer in Touch: a Survey, by Jing Gao et al.
Transformer in Touch: A Survey
by Jing Gao, Ning Cheng, Bin Fang, Wenjuan Han
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Transformer model, initially successful in natural language processing, has shown promise in tactile perception. This review outlines the application and development of Transformers in tactile technology. We introduce the self-attention mechanism and large-scale pre-training behind the Transformer’s success. The application of Transformers in object recognition, cross-modal generation, and object manipulation is explored, highlighting core methodologies, performance benchmarks, and design highlights. Finally, potential areas for further research are suggested to encourage the use of Transformer models in tactile technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Transformer model has been used for language tasks, but it can also help with touch-based tasks like recognizing objects or understanding how things feel. This review looks at how Transformers work and what they’ve done so far in this area. It shows how the model’s self-attention mechanism and large-scale training have made it good at certain tasks. The review also discusses how Transformers have been used for object recognition, generating sounds to match what we touch, and manipulating objects remotely. Finally, it suggests some ideas for future research to make Transformers even better for these types of tasks. |
Keywords
» Artificial intelligence » Natural language processing » Self attention » Transformer
