Summary of Condo: Continual Domain Expansion For Absolute Pose Regression, by Zijun Li et al.
ConDo: Continual Domain Expansion for Absolute Pose Regression
by Zijun Li, Zhipeng Cai, Bochun Yang, Xuelun Shen, Siqi Shen, Xiaoliang Fan, Michael Paulitsch, Cheng Wang
First submitted to arxiv on: 18 Dec 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 The paper proposes a novel approach called Continual Domain Expansion (ConDo) for Absolute Pose Regression (APR), which enables scene-dependent models to adapt to continually changing environments. The existing APR methods are prone to overfitting, leading to catastrophic failures on novel data. ConDo collects unlabeled inference data and updates the deployed model by distilling knowledge from scene-agnostic localization methods. This approach is shown to be effective in expanding the generalization domain of APR, outperforming baselines across various architectures, scene types, and data changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for cameras to understand their environment, even when things change around them. The current method for doing this, called Absolute Pose Regression (APR), has a big problem: it doesn’t work well when the environment changes unexpectedly. This is because APR becomes too specialized in one specific situation and can’t adapt to new situations. To fix this, the authors developed a new approach called Continual Domain Expansion (ConDo). ConDo collects new data from the camera’s experiences and uses that information to improve its understanding of the world. This allows it to work better even when things change around it. |
Keywords
» Artificial intelligence » Generalization » Inference » Overfitting » Regression
