ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2017
The Incremental Multiresolution Matrix Factorization Algorithm.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 0 citations in OpenAlex.
- Multi Resolution Analysis (MRA) for Approximate Self-Attention.Proceedings of machine learning research · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices - an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct "global" factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision.
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Registered trials
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