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Found 7 papers, 2 papers with code
Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models
This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power.
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.
What Affects Learned Equivariance in Deep Image Recognition Models?
We therefore investigate what can increase the learned equivariance in neural networks, and find that data augmentation, reduced model capacity and inductive bias in the form of convolutions induce higher learned equivariance in neural networks.
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.
Domain Adaptation for Rare Classes Augmented with Synthetic Samples
While domain adaptation is generally applied on completely synthetic source domains and real target domains, we explore how domain adaptation can be applied when only a single rare class is augmented with simulated samples.
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
In this work, we propose FlexConv, a novel convolutional operation with which high bandwidth convolutional kernels of learnable kernel size can be learned at a fixed parameter cost.
VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges.
