Search Results for author: Arnold Smeulders

Found 24 papers, 10 papers with code

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

Human-Object Interaction Detection via Weak Supervision

no code implementations1 Dec 2021 Mert Kilickaya, Arnold Smeulders

iii) We evaluate Align-Former on HICO-DET [5] and V-COCO [13], and show that Align-Former outperforms existing image-level supervised HO-I detectors by a large margin (4. 71% mAP improvement from 16. 14% to 20. 85% on HICO-DET [5]).

Human-Object Interaction Detection

Wiggling Weights to Improve the Robustness of Classifiers

1 code implementation18 Nov 2021 Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders

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

no code implementations ICLR 2019 Jan Jetze Beitler, Ivan Sosnovik, Arnold Smeulders

We consider the problem of information compression from high dimensional data.

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.

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.

Built-in Elastic Transformations for Improved Robustness

no code implementations20 Jul 2021 Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders

We focus on building robustness in the convolutions of neural visual classifiers, especially against natural perturbations like elastic deformations, occlusions and Gaussian noise.

Data Augmentation

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.

Natural Perturbed Training for General Robustness of Neural Network Classifiers

no code implementations21 Mar 2021 Sadaf Gulshad, Arnold Smeulders

For Cifar-10 and STL-10 natural perturbed training even improves the accuracy for clean data and reaches the state of the art performance.

Self-Selective Context for Interaction Recognition

no code implementations17 Oct 2020 Mert Kilickaya, Noureldien Hussein, Efstratios Gavves, Arnold Smeulders

Our experiments show that SSC leads to an important increase in interaction recognition performance, while using much fewer parameters.

Human-Object Interaction Detection

Adversarial and Natural Perturbations for General Robustness

no code implementations3 Oct 2020 Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders

In this paper we aim to explore the general robustness of neural network classifiers by utilizing adversarial as well as natural perturbations.

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

Explaining with Counter Visual Attributes and Examples

1 code implementation27 Jan 2020 Sadaf Gulshad, Arnold Smeulders

Hence, inspired by the way of human explanations in this paper we provide attribute-based and example-based explanations.

Understanding Misclassifications by Attributes

1 code implementation15 Oct 2019 Sadaf Gulshad, Zeynep Akata, Jan Hendrik Metzen, Arnold Smeulders

We study the changes in attributes for clean as well as adversarial images in both standard and adversarially robust networks.

Scale-Equivariant Steerable Networks

1 code implementation ICLR 2020 Ivan Sosnovik, Michał Szmaja, Arnold Smeulders

The effectiveness of Convolutional Neural Networks (CNNs) has been substantially attributed to their built-in property of translation equivariance.

Image Classification Translation

Interpreting Adversarial Examples with Attributes

1 code implementation17 Apr 2019 Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders, Zeynep Akata

Deep computer vision systems being vulnerable to imperceptible and carefully crafted noise have raised questions regarding the robustness of their decisions.

General Classification

i-RevNet: Deep Invertible Networks

2 code implementations ICLR 2018 Jörn-Henrik Jacobsen, Arnold Smeulders, Edouard Oyallon

An analysis of i-RevNets learned representations suggests an alternative explanation for the success of deep networks by a progressive contraction and linear separation with depth.

A Biologically Plausible Model for Rapid Natural Scene Identification

no code implementations NeurIPS 2009 Sennay Ghebreab, Steven Scholte, Victor Lamme, Arnold Smeulders

From these neural measurements and the contrast statistics of the natural image stimuli, we derive an across subject Weibull response model.


The Distribution Family of Similarity Distances

no code implementations NeurIPS 2007 Gertjan Burghouts, Arnold Smeulders, Jan-Mark Geusebroek

This fundamental insight opens new directions in the assessment of feature similarity, with projected improvements in object and scene recognition algorithms.

Object Recognition Scene Recognition

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