6 citations · 14 across the 10 of their papers we have counts for
11 papers
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…
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…
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…
Hierarchical Explanations for Video Action Recognition
Sadaf Gulshad, Teng Long, Nanne van Noord
To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods of…
Wiggling Weights to Improve the Robustness of Classifiers
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
Robustness against unwanted perturbations is an important aspect of deploying neural network classifiers in the real world. Common natural perturbations include noise, saturation,…
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.…
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…
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…
Explaining with Counter Visual Attributes and Examples
Sadaf Gulshad, Arnold Smeulders
In this paper, we aim to explain the decisions of neural networks by utilizing multimodal information. That is counter-intuitive attributes and counter visual examples which appear…
Understanding Misclassifications by Attributes
Sadaf Gulshad, Zeynep Akata, Jan Hendrik Metzen +1
In this paper, we aim to understand and explain the decisions of deep neural networks by studying the behavior of predicted attributes when adversarial examples are introduced. We…
Interpreting Adversarial Examples with Attributes
Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders +1
Deep computer vision systems being vulnerable to imperceptible and carefully crafted noise have raised questions regarding the robustness of their decisions. We take a step back an…