24 datasets found

The Pascal VOC Challenge dataset is a benchmark for object detection and image segmentation.

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

Semantic segmentation is one of the most fundamental tasks that aims to classify every pixel of a given image into a specific class. It is widely applied to many applications...

The Pascal VOC, Pascal Context, COCO-Object, Cityscapes, and ADE20k datasets are used for evaluation of the proposed method.

The MS COCO 2014 Dataset contains images of 91 object categories, which contains 82783 training images, 40504 validation images and 40775 testing images.

Panoptic segmentation aims to unify instance and semantic segmentation in the same framework. Existing works propose to merge instance and semantic segmentation using...

Panoptic segmentation aims to unify instance and semantic segmentation in the same framework. Existing works propose to merge instance and semantic segmentation using...

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...

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

The MS COCO dataset is a large benchmark for image captioning, containing 328K images with 5 caption descriptions each.

The COD10K dataset is currently the largest challenging dataset for COD, containing 10K images with dense annotations.

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.

Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.

The dataset used in the paper is a benchmark for referring expression grounding, containing 142,210 referring expressions for 50,000 referents in 19,994 images.

Publicly available plant image datasets are crucial in precision agriculture as they reduce the time and effort spent on data collection and preparation. Also, more data enable...

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,...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

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...