Summary of Meta-optimization For Higher Model Generalizability in Single-image Depth Prediction, by Cho-ying Wu et al.
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction
by Cho-Ying Wu, Yiqi Zhong, Junying Wang, Ulrich Neumann
First submitted to arxiv on: 12 May 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to improving the generalizability of single-image depth prediction models to unseen datasets, focusing on in-the-wild robustness. By leveraging gradient-based meta-learning, the authors develop a method that achieves higher performance on zero-shot cross-dataset inference tasks. Unlike traditional image classification problems, depth prediction involves pixel-level continuous range values and complex mappings between images and depths. The paper proposes a fine-grained task approach, treating each RGB-D pair as a separate task in the meta-optimization process. Experimental results show that meta-learning with limited data induces a better prior (+29.4%) and that using meta-learned weights as initialization for supervised learning consistently outperforms baseline models. The paper also introduces zero-shot cross-dataset protocols to evaluate robustness and demonstrates higher generalizability and accuracy compared to traditional indoor-depth methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make single-image depth prediction models better at working with new, unseen data. They do this by using a special kind of learning called meta-learning that helps the model learn how to adapt to different situations. The problem is that depth prediction involves predicting numbers that can range from small to very large, and there are many ways for these numbers to change between images. The paper proposes a new way of thinking about this task as many smaller tasks, each involving one image and its corresponding depth values. This approach helps the model learn how to adapt better to new data without needing any additional training or information. The results show that this method is more accurate and reliable than traditional methods. |
Keywords
» Artificial intelligence » Image classification » Inference » Meta learning » Optimization » Supervised » Zero shot
