Unsupervised Object Segmentation

DUTS is a saliency detection dataset containing 10,553 training images and 5,019 test images. All training images are collected from the ImageNet DET training/val sets, while test images are collected from the ImageNet DET test set and the SUN data set. Both the training and test set contain very challenging scenarios for saliency detection. Accurate pixel-level ground truths are manually annotated by 50 subjects. Source: http://saliencydetection.net/duts/ Image Source: https://ieeexplore.ieee.org/document/8099887

DAVIS16 is a dataset for video object segmentation which consists of 50 videos in total (30 videos for training and 20 for testing). Per-frame pixel-wise annotations are offered. Source: Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation Image Source: https://davischallenge.org/

The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is an extension of the BMS dataset with 33 additional video sequences. A total of 720 frames is annotated. It has pixel-accurate segmentation annotations of moving objects. FBMS-59 comes with a split into a training set and a test set. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/ Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/

SegTrack v2 is a video segmentation dataset with full pixel-level annotations on multiple objects at each frame within each video. Source: https://web.engr.oregonstate.edu/~lif/SegTrack2/dataset.html Image Source: https://www.researchgate.net/publication/325842926_Semantic_Video_Segmentation_A_Review_on_Recent_Approaches

The Extended Complex Scene Saliency Dataset (ECSSD) is comprised of complex scenes, presenting textures and structures common to real-world images. ECSSD contains 1,000 intricate images and respective ground-truth saliency maps, created as an average of the labeling of five human participants. Source: SAD: Saliency-based Defenses Against Adversarial Examples

ClevrTex is a new benchmark designed as the next challenge to compare, evaluate and analyze algorithms for unsupervised multi-object segmentation. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. Image source: Karazija et al.

A simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. Source: ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences. A total of 720 frames is annotated. FBMS-59 comes with a split into a training set and a test set. Typical challenges appear in both sets. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html

The ObjectsRoom dataset is based on the MuJoCo environment used by the Generative Query Network [4] and is a multi-object extension of the 3d-shapes dataset. The training set contains 1M scenes with up to three objects. We also provide ~1K test examples for the following variants: 2.1 Empty room: scenes consist of the sky, walls, and floor only. 2.2 Six objects: exactly 6 objects are visible in each image. 2.3 Identical color: 4-6 objects are placed in the room and have an identical, randomly sampled color. Datapoints consist of an image and fixed number of masks. The first four masks correspond to the sky, floor, and two halves of the wall respectively. The remaining masks correspond to the foreground objects. Source: Objects Room