Ivan Sosnovik

Learning to Summarize Videos by Contrasting Clips

Ivan Sosnovik, Artem Moskalev, Cees Kaandorp, Arnold Smeulders

Preprint, 2023

In this paper, we formulate video summarization as a contrastive learning problem. We implement the main building blocks which allows one to convert any video analysis model into an effective video summarizer.

LieGG: Studying Learned Lie Group Generators

Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, Arnold Smeulders

NeurIPS, 2022

We present LieGG, a method to extract symmetries learned by neural networks and to evaluate the degree to which a network is invariant to them. With LieGG, one can explicitly retrieve learned invariances in a form of Lie-group generators.

Contrasting quadratic assignments for set-based representation learning

Artem Moskalev, Ivan Sosnovik, Volker Fischer, Arnold Smeulders

ECCV, 2022

We go beyond contrasting individual pairs of objects by focusing on contrasting objects as sets. We use combinatorial quadratic assignment theory and derive set-contrastive objective as a regularizer for contrastive learning methods.

Wiggling Weights to Improve the Robustness of Classifiers

Sadaf Gulshad*, Ivan Sosnovik*, Arnold Smeulders

Preprint, 2021

While many approaches for robustness train the network by providing augmented data to the network, we aim to integrate perturbations in the network architecture to achieve improved and more general robustness.

DISCO: accurate Discrete Scale Convolution (Best Paper Award!!!)

Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

BMVC, Oral, 2021

We develop a better class of discrete scale equivariant CNNs, which are more accurate and faster than all previous methods. As a result of accurate scale analysis, they allow for a biased scene geometry estimation almost for free.

Relational Prior for Multi-Object Tracking

Artem Moskalev, Ivan Sosnovik, Arnold Smeulders

ICCV VIPriors Workshop, Oral, 2021

Tracking multiple objects individually differs from tracking groups of related objects. We propose a plug-in Relation Encoding Module which encodes relations between tracked objects to improve multi-object tracking.

How to Transform Kernels for Scale-Convolutions

Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

ICCV VIPriors Workshop, 2021

To reach accurate scale equivariance, we derive general constraints under which scale-convolution remains equivariant to discrete rescaling. We find the exact solution for all cases where it exists, and compute the approximation for the rest.

Two is a Crowd: Tracking Relations in Videos

Artem Moskalev, Ivan Sosnovik, Arnold Smeulders

Preprint, 2021

We propose a plug-in Relation Encoding Module to learn a spatio-temporal graph of correspondences between objects within and between frames. The proposed module allows for tracking even fully occluded objects by utilizing relational cues.

Built-in Elastic Transformations for Improved Robustness

Sadaf Gulshad*, Ivan Sosnovik*, Arnold Smeulders

Preprint, 2021

We present elastically-augmented convolutions (EAConv) by parameterizing filters as a combination of fixed elastically-perturbed bases functions and trainable weights for the purpose of integrating unseen viewpoints in CNNs.

Scale Equivariance Improves Siamese Tracking

Ivan Sosnovik*, Artem Moskalev*, Arnold Smeulders

WACV, 2021

In this paper, we develop the theory for scale-equivariant Siamese trackers. We also provide a simple recipe for how to make a wide range of existing trackers scale-equivariant to capture the natural variations of the target a priori.

Scale-Equivariant Steerable Networks

Ivan Sosnovik, Michał Szmaja, Arnold Smeulders

ICLR, 2020

We introduce the general theory for building scale-equivariant convolutional networks with steerable filters. We develop scale-convolution and generalize other common blocks to be scale-equivariant.

Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry Vetrov

ICML INNF, 2019

This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels.

Neural Networks for Topology Optimization

Ivan Sosnovik, Ivan Oseledets

Russian Journal of Numerical Analysis and Mathematical Modelling 34 (4), 2019

In this research, we propose a deep learning based approach for speeding up the topology optimization methods. We formulate the problem as image segmentation and leverage the power of deep learning to perform pixel-wise image labeling.

PIE: Pseudo-Invertible Encoder

Jan Jetze Beitler*, Ivan Sosnovik*, Arnold Smeulders

Preprint, 2018

We introduce a new class of likelihood-based autoencoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders. We provide the theoretical explanation of their principles.