Summary of Adapting Pretrained Vits with Convolution Injector For Visuo-motor Control, by Dongyoon Hwang et al.
Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control
by Dongyoon Hwang, Byungkun Lee, Hojoon Lee, Hyunseung Kim, Jaegul Choo
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 paper introduces Convolution Injector (CoIn), an add-on module designed to adapt Vision Transformers (ViT) for visuo-motor control tasks. By injecting convolutions rich in locality and equivariance biases into a pretrained ViT, CoIn enhances the model’s performance across various control tasks within three distinct domains: Adroit, MetaWorld, and DMC. The evaluation demonstrates consistent improvements in control task performance using three different types of pre-trained ViTs (CLIP, MVP, VC-1). This suggests that providing control-centric biases to pretrained ViTs can be effective for visuo-motor control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to make computer vision models work better with robots. These models are called Vision Transformers and they’re good at doing things like recognizing pictures. But when we try to use them to control robots, they don’t do as well because they’re not designed for that kind of task. The solution is an add-on module that gives the model a special set of skills that’s helpful for controlling robots. This module is called Convolution Injector and it makes the model better at doing tasks like picking up objects or moving around. |
Keywords
» Artificial intelligence » Vit
