Search Results for author: Arnold Smeulders

Found 18 papers, 5 papers with code

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.

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

no code implementations4 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

no code implementations 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

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.

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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