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Found 13 papers, 8 papers with code
Learning to Summarize Videos by Contrasting Clips
Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one.
LieGG: Studying Learned Lie Group Generators
Symmetries built into a neural network have appeared to be very beneficial for a wide range of tasks as it saves the data to learn them.
Contrasting quadratic assignments for set-based representation learning
The views are ordered in pairs, such that they are either positive, encoding different views of the same object, or negative, corresponding to views of different objects.
Wiggling Weights to Improve the Robustness of Classifiers
To demonstrate that wiggling the weights consistently improves classification, we choose a standard network and modify it to a transform-augmented network.
PIE: Pseudo-Invertible Encoder
We consider the problem of information compression from high dimensional data.
How to Transform Kernels for Scale-Convolutions
We aim for accurate scale-equivariant convolutional neural networks (SE-CNNs) applicable for problems where high granularity of scale and small kernel sizes are required.
Built-in Elastic Transformations for Improved Robustness
We focus on building robustness in the convolutions of neural visual classifiers, especially against natural perturbations like elastic deformations, occlusions and Gaussian noise.
DISCO: accurate Discrete Scale Convolutions
In recent work scale equivariance was added to convolutional neural networks.
Scale Equivariance Improves Siamese Tracking
We develop the theory for scale-equivariant Siamese trackers, and provide a simple recipe for how to make a wide range of existing trackers scale-equivariant.
Ranked #1 on
Visual Object Tracking
on OTB-2013
Scale-Equivariant Steerable Networks
The effectiveness of Convolutional Neural Networks (CNNs) has been substantially attributed to their built-in property of translation equivariance.
Ranked #33 on
Image Classification
on STL-10
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning.
Neural networks for topology optimization
The main novelty of this work is to state the problem as an image segmentation task.
