Summary of Deep Extrinsic Manifold Representation For Vision Tasks, by Tongtong Zhang et al.
Deep Extrinsic Manifold Representation for Vision Tasks
by Tongtong Zhang, Xian Wei, Yuanxiang Li
First submitted to arxiv on: 31 Mar 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 introduces a novel approach called Deep Extrinsic Manifold Representation (DEMR) for training neural networks to generate representations of non-Euclidean data. The DEMR trick incorporates extrinsic manifold embedding into deep neural networks, allowing for the generation of manifold representations without directly optimizing complex geodesic loss. This method focuses on optimizing the computation graph within an embedded Euclidean space, making it adaptable to various architectural requirements. Experimental results demonstrate that DEMR effectively adapts to point cloud alignment and illumination subspace learning tasks, producing outputs on the SE(3) manifold and Grassmann manifold, respectively. The proposed concept shows promise for visual tasks in this context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a computer to understand shapes and patterns that don’t fit into our usual 2D or 3D spaces. That’s what this paper is all about. It introduces a new way to train computers to recognize and generate these special shapes, called “manifolds.” This approach, called DEMR, helps computers learn to adapt to different situations where these shapes are involved. The results show that DEMR can successfully align 3D point clouds and even learn about different lighting conditions. This is important because it could have applications in fields like computer vision and robotics. |
Keywords
» Artificial intelligence » Alignment » Embedding
