Summary of Kick Back & Relax++: Scaling Beyond Ground-truth Depth with Slowtv & Cribstv, by Jaime Spencer et al.
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV
by Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers tackle the challenge of scaling self-supervised learning for computer vision tasks, specifically monocular depth estimation (MDE). By leveraging large quantities of data without relying on ground-truth annotations, they aim to unlock generic computer vision systems. However, existing datasets have focused solely on urban driving in densely populated cities, limiting model generalizability beyond this domain. The paper proposes a solution to this problem by introducing diverse training data, enabling MDE models to generalize better. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on developing self-supervised learning for computer vision tasks. Right now, it’s limited because we don’t have enough training data without labels. To fix this, they’re trying to get more diverse data so that the models can learn to do things in different situations. This is important because we want our computers to be able to understand what’s happening around them, like a self-driving car seeing the road ahead. |
Keywords
» Artificial intelligence » Depth estimation » Self supervised
