Learning to Relate Images
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Review of relational deep-learning methods for image correspondence; a machine-learning methods contribution in its own domain.
This reviews machine-learning methods for image relations, not methods or practices of research.
Computer-vision methods review on learning image correspondences; domain ML/vision, not metaresearch.
Abstract
A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. Recently, there has been increasing interest in learning to infer correspondences from data using relational, spatiotemporal, and bilinear variants of deep learning methods. These methods use multiplicative interactions between pixels or between features to represent correlation patterns across multiple images. In this paper, we review the recent work on relational feature learning, and we provide an analysis of the role that multiplicative interactions play in learning to encode relations. We also discuss how square-pooling and complex cell models can be viewed as a way to represent multiplicative interactions and thereby as a way to encode relations.
Stored with the screening record, where it is evidence for the labels above.
The record
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Topic
- Cell Image Analysis Techniques
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- Université de Montréal
- Funders
- —
- Keywords
- ENCODEArtificial intelligenceComputer sciencePoolingFeature (linguistics)Pattern recognition (psychology)ModalitiesMultiplicative functionBilinear interpolationDeep learningComputer visionMachine learningMathematics
- Has abstract in OpenAlex
- yes