Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction

• URL: http://arxiv.org/abs/2305.07269v2
• Date: Tue, 30 Jan 2024 08:49:14 GMT
• Title: Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction
• Authors: Cho-Ying Wu, Yiqi Zhong, Junying Wang, Ulrich Neumann
• Abstract summary: We leverage gradient-based meta-learning for higher generalizability on zero-shot cross-dataset inference. Unlike the most-studied image classification in meta-learning, depth is pixel-level continuous range values. We propose fine-grained task that treats each RGB-D pair as a task in our meta-optimization.
• Score: 19.469860191876876
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Model generalizability to unseen datasets, concerned with in-the-wild robustness, is less studied for indoor single-image depth prediction. We leverage gradient-based meta-learning for higher generalizability on zero-shot cross-dataset inference. Unlike the most-studied image classification in meta-learning, depth is pixel-level continuous range values, and mappings from each image to depth vary widely across environments. Thus no explicit task boundaries exist. We instead propose fine-grained task that treats each RGB-D pair as a task in our meta-optimization. We first show meta-learning on limited data induces much better prior (max +29.4\%). Using meta-learned weights as initialization for following supervised learning, without involving extra data or information, it consistently outperforms baselines without the method. Compared to most indoor-depth methods that only train/ test on a single dataset, we propose zero-shot cross-dataset protocols, closely evaluate robustness, and show consistently higher generalizability and accuracy by our meta-initialization. The work at the intersection of depth and meta-learning potentially drives both research streams to step closer to practical use.

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