22 datasets found

Object detection dataset used for training and testing the proposed alert system.

Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature...

The PASCAL Visual Object Classes Challenge (VOC) is a benchmark dataset for object detection and semantic segmentation.

The dataset used in the paper is a synthetic dataset for image-based object detection tasks, specifically instance segmentation and monocular 3D detection.

The dataset used in the paper is the COCO object detection and instance segmentation dataset, and the ADE20K semantic segmentation dataset.

The dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.

CamVid dataset is a benchmark dataset for semantic segmentation. It consists of 700 images with 11 object classes.

The dataset used in the paper is the Pascal VOC 2012 dataset, which is a benchmark for instance segmentation. The dataset consists of 1464 images with 20 class categories and...

The dataset used in the paper is ImageNet-1K, ADE20K, and COCO 2017.

COCO Stuff dataset is an extension of the COCO dataset, 164,000 images covering 171 classes are annotated with segmentation masks.

The PASCAL Context dataset is a benchmark for multi-task learning in computer vision. It contains 10103 images with 5 tasks: semantic segmentation, human body part segmentation,...

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The dataset used in the paper is ImageNet, MS COCO, and Pascal VOC datasets.

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...

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...

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,...

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used it for language modeling and image classification tasks.