Interactive Segmentation

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: https://www.cityscapes-dataset.com/dataset-overview/

The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p. There are 50 video sequences with 3455 densely annotated frames in pixel level. 30 videos with 2079 frames are for training and 20 videos with 1376 frames are for validation. Source: TENet: Triple Excitation Network for Video Salient Object Detection

The PASCAL Visual Object Classes (VOC) 2012 dataset contains 20 object categories including vehicles, household, animals, and other: aeroplane, bicycle, boat, bus, car, motorbike, train, bottle, chair, dining table, potted plant, sofa, TV/monitor, bird, cat, cow, dog, horse, sheep, and person. Each image in this dataset has pixel-level segmentation annotations, bounding box annotations, and object class annotations. This dataset has been widely used as a benchmark for object detection, semantic segmentation, and classification tasks. The PASCAL VOC dataset is split into three subsets: 1,464 images for training, 1,449 images for validation and a private testing set. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey Image Source: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/examples/images/sheep_06.jpg

The Semantic Boundaries Dataset (SBD) is a dataset for predicting pixels on the boundary of the object (as opposed to the inside of the object with semantic segmentation). The dataset consists of 11318 images from the trainval set of the PASCAL VOC2011 challenge, divided into 8498 training and 2820 test images. This dataset has object instance boundaries with accurate figure/ground masks that are also labeled with one of 20 Pascal VOC classes. Source: Weakly Supervised Object Boundaries Image Source: http://home.bharathh.info/pubs/codes/SBD/download.html

A dataset for interactive segmentation with simulated initial masks.

We provide two image stacks where each contains 20 sections from serial section Transmission Electron Microscopy (ssTEM) of the Drosophila melanogaster third instar larva ventral nerve cord. Both stacks measure approx. 4.7 x 4.7 x 1 microns with a resolution of 4.6 x 4.6 nm/pixel and section thickness of 45-50 nm. In addition to the raw image data, we provide for the first stack a dense labeling of neuron membranes (including orientation and junction), mitochondria, synapses and glia/extracellular space. The first stack serves as a training dataset, and a second stack of the same dimension can be used as a test dataset. Source: https://figshare.com/articles/dataset/Segmented_anisotropic_ssTEM_dataset_of_neural_tissue/856713 Image source: https://figshare.com/articles/dataset/Segmented_anisotropic_ssTEM_dataset_of_neural_tissue/856713

PUMaVOS is a dataset of challenging and practical use cases inspired by the movie production industry. Partial and Unusual Masks for Video Object Segmentation (PUMaVOS) dataset has the following properties: - 24 videos, 21187 densely-annotated frames; - Covers complex practical use cases such as object parts, frequent occlusions, fast motion, deformable objects and more; - Average length of the video is 883 frames or 29s, with the longer ones spanning 1min; - Fully densely annotated at 30FPS; - Benchmark-oriented: no separation into training/test, designed to be as diverse as possible to test your models; - 100% open and free to download.

We conducted a large crowdsourcing study of click patterns in an interactive segmentation scenario and collected 475K real-user clicks. Drawing on ideas from saliency tasks, we develop a clickability model that enables sampling clicks, which closely resemble actual user inputs. Using our model and dataset, we propose RClicks benchmark for a comprehensive comparison of existing interactive segmentation methods on realistic clicks. Specifically, we evaluate not only the average quality of methods, but also the robustness w.r.t. click patterns.