Search Results for author: Artem Moskalev

Found 8 papers, 5 papers with code

On genuine invariance learning without weight-tying

1 code implementation7 Aug 2023 Artem Moskalev, Anna Sepliarskaia, Erik J. Bekkers, Arnold Smeulders

We demonstrate that even when a network learns to correctly classify samples on a group orbit, the underlying decision-making in such a model does not attain genuine invariance.

Decision Making

Learning to Summarize Videos by Contrasting Clips

no code implementations12 Jan 2023 Ivan Sosnovik, Artem Moskalev, Cees Kaandorp, Arnold Smeulders

Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one.

Contrastive Learning Unsupervised Video Summarization

LieGG: Studying Learned Lie Group Generators

1 code implementation9 Oct 2022 Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, Arnold Smeulders

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

1 code implementation31 May 2022 Artem Moskalev, Ivan Sosnovik, Volker Fischer, Arnold Smeulders

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.

Contrastive Learning Metric Learning +1

Two is a crowd: tracking relations in videos

no code implementations11 Aug 2021 Artem Moskalev, Ivan Sosnovik, Arnold Smeulders

Tracking multiple objects individually differs from tracking groups of related objects.

Object Relation +1

How to Transform Kernels for Scale-Convolutions

no code implementations ICCVW 2021 Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

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.

DISCO: accurate Discrete Scale Convolutions

1 code implementation4 Jun 2021 Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

In recent work scale equivariance was added to convolutional neural networks.

Scale Equivariance Improves Siamese Tracking

1 code implementation17 Jul 2020 Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

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.

Translation Visual Object Tracking +1

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