Summary of Self-supervised Visual Learning From Interactions with Objects, by Arthur Aubret et al.
Self-supervised visual learning from interactions with objects
by Arthur Aubret, Céline Teulière, Jochen Triesch
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper explores whether incorporating human-like actions into self-supervised learning (SSL) can enhance visual representation learning, which has been a challenge in achieving robustness comparable to human vision. The authors propose a new loss function that learns visual and action embeddings by aligning the performed actions with representations of two images from the same video clip. This approach structures the latent visual representation based on the observed actions. Experimental results show that this method consistently outperforms previous methods on downstream category recognition tasks, suggesting that embodied interactions with objects can improve SSL for object categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can make computers learn to recognize objects better by giving them more information like what humans do when learning. Humans don’t just look at an object; they move around it and change their perspective. This helps us learn more efficiently. The researchers in this study wanted to see if computers could also benefit from this type of interaction. They looked at videos where people were moving objects or changing their viewpoint, and then used a special way of learning that took into account these actions. Their results showed that this new approach was better than previous methods at recognizing different types of objects. This is important because it can help us develop computers that are more like humans in how they learn. |
Keywords
» Artificial intelligence » Loss function » Representation learning » Self supervised
