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Cube+

The dataset is used for learning color constancy.

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RenderedWB

The dataset is used for auto-white balance correction.

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DeNIM

Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios.

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CMU Motion Capture Database

The dataset used for training and testing the model consists of 3D walking motion capture data of three different participants. The data is edited to form a continuous cycle...

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High-accuracy vision-based attitude estimation system for air-bearing spacecr...

A vision-based method to precisely compute the attitude of 3-DoF rotational air-bearing platforms.

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MNIST data set

The MNIST data set is a dataset used for testing the performance of the DisDF.

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ConvNeXt

The dataset used in this paper is a convolutional neural network (CNN) model, specifically ConvNeXt.

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ResNet-34

The dataset used in this paper is a convolutional neural network (CNN) model, specifically ResNet-34, and its layers 20 and 28.

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Long Range 3D with Quadocular Thermal (LWIR) Camera

Long Wave Infrared (LWIR) cameras provide images regardless of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights.

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Active Perception

The dataset used in the paper is not explicitly mentioned, but it is implied that it is a collection of images and videos of a kitchen scene.

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Appearance-based Gaze Estimation using Dilated-Convolutions

The dataset is used for appearance-based gaze estimation with dilated-convolutions.

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GEDDnet: A Network for Gaze Estimation with Dilation and Decomposition

The dataset is used for appearance-based gaze estimation with dilation and decomposition.

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DelugeNets: Deep Networks with Efﬁcient and Flexible Cross-layer Information ...

Deluge Networks are deep neural networks which efficiently facilitate massive cross-layer information inﬂows from preceding layers to succeeding layers.

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SHIFT and BDD-A datasets

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the SHIFT and Berkeley DeepDrive Attention (BDD-A) datasets.

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RANSAC 2020 Dataset

The dataset used in the paper is a real-world dataset for relative pose estimation, consisting of image pairs from the RANSAC 2020 dataset.

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Synthetic Relative Pose Estimation Dataset

The dataset used in the paper is a synthetic dataset for relative pose estimation, consisting of 3000 random minimal problems for both calibrated and uncalibrated cases.