10 datasets found

The KITTI-C dataset is a robustness benchmark for outdoor monocular depth estimation under data corruptions.

The dataset used in this paper is a low-rank matrix M, which can be decomposed into a low-rank component L∗ and a sparse error matrix S∗. The authors use this dataset to test...

The MMR benchmark is designed to evaluate MLLMs' comprehension of visual content and robustness against misleading questions, ensuring models truly leverage multimodal inputs...

CIFAR-10-C and CIFAR-100-C are robustness benchmarks consisting of 19 corruptions types with five levels of severities.

Dataset bias is a significant problem in training fair classifiers. When attributes unrelated to classification exhibit strong biases towards certain classes, classifiers...

A general framework for learning perturbation sets from data when the perturbation cannot be mathematically-defined.

The LAV dataset is used to evaluate the robustness of the proposed Penalty-based Imitation Learning with Cross Semantics Generation approach.

The Imbalanced Gradients dataset is a benchmark for evaluating the robustness of deep neural networks.

3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these...

The dataset used in the paper is the ImageNet-C dataset, which is a dataset of images corrupted with various types of noise and occlusions.