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Found 8 papers, 5 papers with code
Deep Continuous Networks
CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research.
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks.
Heart rate estimation in intense exercise videos
Existing work can robustly measure heart rate under some degree of motion by face tracking.
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
The second edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges featured five data-impaired challenges, where models are trained from scratch on a reduced number of training samples for various key computer vision tasks.
Frequency learning for structured CNN filters with Gaussian fractional derivatives
Frequency information lies at the base of discriminating between textures, and therefore between different objects.
Resolution learning in deep convolutional networks using scale-space theory
We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet: a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters.
Spectral Leakage and Rethinking the Kernel Size in CNNs
We show that the small size of CNN kernels make them susceptible to spectral leakage, which may induce performance-degrading artifacts.
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli.
