Summary of Boosting Generalizability Towards Zero-shot Cross-dataset Single-image Indoor Depth by Meta-initialization, By Cho-ying Wu et al.
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization
by Cho-Ying Wu, Yiqi Zhong, Junying Wang, Ulrich Neumann
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a novel approach to single-image depth estimation for indoor robots, focusing on model generalizability to unseen datasets. By leveraging gradient-based meta-learning, the authors demonstrate higher generalizability on zero-shot cross-dataset inference. The proposed method treats each RGB-D mini-batch as a task in the meta-learning formulation and shows that fine-tuning on meta-learned initialization consistently outperforms baselines without the meta approach. The paper also introduces zero-shot cross-dataset protocols to validate higher generalizability induced by the meta-initialization. This work has the potential to drive both research in depth estimation and meta-learning closer to practical robotic and machine perception usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots navigate indoor spaces better by improving how they guess distances from pictures. The usual way of doing this doesn’t focus on being good at guessing distances for new, unseen scenes. This paper uses a special learning technique called gradient-based meta-learning to make the robot’s depth guesses more reliable and accurate even when it hasn’t seen those specific scenes before. The authors test their approach and show that it works well by comparing it to other ways of doing things. This research could lead to better robots and computer vision systems. |
Keywords
» Artificial intelligence » Depth estimation » Fine tuning » Inference » Meta learning » Zero shot
