150 datasets found

Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance...

The proposed Concentrated-Comprehensive Convolution (C3) block for lightweight semantic segmentation.

The SYNTHIA dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

The GTA5 dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

PASCAL VOC 2007 dataset is a widely used dataset for object detection and semantic segmentation. We use all the split sets (training, validation, testing) in the VOC2007 dataset...

The dataset is generated using a latent diffusion model, specifically Stable Diffusion 2.1, and is used for semantic segmentation tasks.

The Replica dataset contains 18 various highly photo-realistic indoor environments. It provides dense-mesh, high-resolution RGBD images and a large range of instance annotations...

The ScanNet dataset is a large-scale indoor dataset composed of monocular sequences with ground truth poses and depth images.

A dataset for semantic segmentation of high-resolution remote sensing images.

A benchmark dataset for semantic segmentation of high-resolution remote sensing images.

A point cloud dataset with large quantity of dynamic instances, consisting of 2,988 real-world scans with point-level annotations.

The LoveDA dataset contains high-spatial-resolution images from three different cities, focusing on improving the generalization capability of model from different urban and...

Semantic segmentation of remote sensing imagery generally revolves two primary approaches: pixel-based and object-based methods.

The ISPRS Vaihingen dataset consists of remotely sensed imagery with a spatial resolution of 9 centimeters, and includes 6 semantic classes.

Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique...

A real-world dataset for multi-object shape completion, featuring 33 objects (e.g., toy, household, and industrial objects).

The dataset used for 6D object pose estimation, consisting of images of 16 objects with varying levels of occlusion.

The TrashNet dataset contains images of waste, focusing on recycling waste classification.

The COCO dataset is a large-scale dataset for object detection, semantic segmentation, and captioning. It contains 80 object categories and 1,000 image instances per category,...

Semantic segmentation is one of the fundamental prob-lems in computer vision, whose task is to assign a seman-tic label to each pixel of an image so that different classes can...