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SIFT1M and GIST1M
The dataset used in this paper is SIFT1M and GIST1M, two large-scale image datasets. -
nuScene Dataset
The nuScene dataset contains images and annotations of objects in the scene. -
DrivingStereo
A real-world stereo dataset containing both indoor and outdoor environments via robot teleoperation. -
Edge-Preserving Image Smoothing Dataset
A dataset for edge-preserving image smoothing, containing 500 training and testing images with a number of representative visual object categories. -
Edge-Preserving Image Smoothing Benchmark
A benchmark for edge-preserving image smoothing for the purpose of quantitative performance evaluation and further advancing the state-of-the-art. -
Stereo Matching
The dataset used in the paper for stereo matching, which is a type of computer vision problem. -
Color Segmentation
The dataset used in the paper for color segmentation, which is a type of computer vision problem. -
Graph matching problems
The dataset used in the paper for graph matching problems, which is a type of computer vision problem. -
Real-Time Resource Allocation for Tracking Systems
A real-life dataset containing approximately 2100 trajectories of people recorded by a camera taking 5120×3840 resolution images running at 6 frames per second. -
ArticulatedFreeFall dataset
The dataset used for testing the gravity-based height estimation method. -
Hand Tremor Amplitude Measurement using Smartphone Videos
Dataset for hand tremor amplitude measurement using smartphone videos -
Variable pixel imaging dataset
The dataset used to test the proposed variable pixel imaging method. -
Variable pixel imaging
The proposed parallel variable pixel arrays system for generating images gives an improved visual quality for the images studied. -
Procedural 3D Terrain Generation using Generative Adversarial Networks
A dataset of procedurally generated satellite images and their corresponding DEMs. -
Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers
A benchmark for assessing the performance of fairness mitigation methods in computer vision. -
Fairness meets Cross-Domain Learning
A new benchmark for assessing the performance of cross-domain learning approaches for unfairness mitigation in computer vision. -
PartNet-Mobility
The PartNet-Mobility dataset is a large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. It consists of 527 objects composed of 2690 parts,... -
7Scenes and Cambridge Landmarks datasets
The 7Scenes and Cambridge Landmarks datasets are used for evaluation of absolute pose estimators.
