34 datasets found

A comprehensive dataset featuring ten distinct hen breeds, capturing unique characteristics and traits of each breed.

The R4R dataset is a larger VLN dataset than R2R and with more complicated navigation paths.

The R2R dataset is a dataset based on real photos taken in indoor environments. It attracts massive attention for its simple-form task, which at the same time requires complex...

The dataset is used for learning color constancy.

The dataset is used for auto-white balance correction.

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

A data basis of irregularly sampled images on a sphere is provided in [4]. It is constructed by projecting the MNIST image digits on d = 4096 points randomly sampled on the 3D...

A novel MicroISP model designed specifically for edge devices, taking into account their computational and memory limitations.

A novel MicroISP model designed specifically for edge devices, taking into account their computational and memory limitations.

The KITTI benchmark dataset is used to evaluate the performance of the proposed method. The dataset contains large-scale outdoor sequences of images captured by a forward-facing...

The dataset used for training and testing the proposed deep co-training method for semi-supervised image segmentation.

The MIT-Adobe FiveK dataset, a large-scale dataset for image segmentation and object detection.

A fast, scalable, and reliable deghosting method for extreme exposure fusion.

An objective deghosting quality metric for HDR images.

Deep high dynamic range imaging of dynamic scenes.

Frame high dynamic range photography.

The FlyingThings3D dataset is a benchmark for stereo matching, consisting of a large collection of images and corresponding disparity maps.

Convolutional neural networks (CNNs) have become the state-of-the-art tool for dealing with unsolved problems in computer vision and image processing.

HICO-Det is a benchmark for Human-Object Interaction (HOI) detection.

Human-Object Interaction (HOI) detection is a significant task to make a machine understand human activities in a static image at a fine-grained level.