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20192025
most citedInterpreting Adversarial Examples with Attributes

6 citations · 10 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CV2025

DArFace: Deformation Aware Robustness for Low Quality Face Recognition

Sadaf Gulshad, Abdullah Aldahlawi

Facial recognition systems have achieved remarkable success by leveraging deep neural networks, advanced loss functions, and large-scale datasets. However, their performance often…

cs.CV2024

The SkatingVerse Workshop & Challenge: Methods and Results

Jian Zhao, Lei Jin, Jianshu Li +16

The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding. The SkatingVerse dataset used for the Skat…

cs.CV2023

The 3rd Anti-UAV Workshop & Challenge: Methods and Results

Jian Zhao, Jianan Li, Lei Jin +20

The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UA…

cs.CV2021

Built-in Elastic Transformations for Improved Robustness

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.…

cs.CV20212 cited

Natural Perturbed Training for General Robustness of Neural Network Classifiers

Sadaf Gulshad, Arnold Smeulders

We focus on the robustness of neural networks for classification. To permit a fair comparison between methods to achieve robustness, we first introduce a standard based on the mens…

cs.CV20202 cited

Adversarial and Natural Perturbations for General Robustness

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. Different from previous works which…