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Visual Features and Their Own Optical Flow.
Alessandro Betti1, Giuseppe Boccignone2, Lapo Faggi1,3
1Department of Information Engineering and Mathematics, Università degli Studi di Siena, Siena, Italy.
Frontiers in Artificial Intelligence
|December 20, 2021
Summary
This study introduces Material Point Invariance, linking visual features to optical flow for better video understanding. This principle helps create more robust neural networks by treating features and velocities as inseparable pairs.
Area of Science:
- Computer Vision
- Computational Neuroscience
- Machine Learning
Background:
- Symmetries and conservation laws are crucial for modeling natural phenomena.
- Convolutional Neural Networks (CNNs) leverage translation equivariance for visual tasks.
- Existing methods struggle with the broad spectrum of transformations in video streams, requiring extensive supervision.
Purpose of the Study:
- To develop a theory for visual feature development based on movement consistency.
- To introduce the principle of Material Point Invariance.
- To explore the generalization of motion invariance and its relation to affordances.
Main Methods:
- Formulating a theory on visual feature development driven by motion.
- Introducing the principle of Material Point Invariance, pairing visual features with optical flow.
- Analyzing feature-velocity interactions and their invariance properties.
Main Results:
- Established that visual features are invariant with respect to their associated optical flow.
- Demonstrated that features and velocities form an indissoluble pair.
- Showcased how motion invariance traits can generalize the concept of affordance.
Conclusions:
- Proposed a visual field theory based on feature-velocity interactions.
- This theory expresses dynamical constraints of motion coherence.
- Potential to discover the joint evolution of visual features and optical flows.
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