Visual Relationship Detection

Visual Genome contains Visual Question Answering data in a multi-choice setting. It consists of 101,174 images from MSCOCO with 1.7 million QA pairs, 17 questions per image on average. Compared to the Visual Question Answering dataset, Visual Genome represents a more balanced distribution over 6 question types: What, Where, When, Who, Why and How. The Visual Genome dataset also presents 108K images with densely annotated objects, attributes and relationships. Source: RaAM: A Relation-aware Attention Model for Visual Question Answering Image Source: Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

The Visual Relationship Dataset (VRD) contains 4000 images for training and 1000 for testing annotated with visual relationships. Bounding boxes are annotated with a label containing 100 unary predicates. These labels refer to animals, vehicles, clothes and generic objects. Pairs of bounding boxes are annotated with a label containing 70 binary predicates. These labels refer to actions, prepositions, spatial relations, comparatives or preposition phrases. The dataset has 37993 instances of visual relationships and 6672 types of relationships. 1877 instances of relationships occur only in the test set and they are used to evaluate the zero-shot learning scenario. Source: Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation Image Source: https://cs.stanford.edu/people/ranjaykrishna/vrd/

ImageNet-VidVRD dataset contains 1,000 videos selected from ILVSRC2016-VID dataset based on whether the video contains clear visual relations. It is split into 800 training set and 200 test set, and covers common subject/objects of 35 categories and predicates of 132 categories. Ten people contributed to labeling the dataset, which includes object trajectory labeling and relation labeling. Since the ILVSRC2016-VID dataset has the object trajectory annotation for 30 categories already, we supplemented the annotations by labeling the remaining 5 categories. In order to save the labor of relation labeling, we labeled typical segments of the videos in the training set and the whole of the videos in the test set.

Open Images is a computer vision dataset covering ~9 million images with labels spanning thousands of object categories. A subset of 1.9M includes diverse annotations types. - 15,851,536 boxes on 600 classes - 2,785,498 instance segmentations on 350 classes - 3,284,280 relationship annotations on 1,466 relationships - 675,155 localized narratives (synchronized voice, mouse trace, and text caption) - 66,391,027 point-level annotations on 5,827 classes - 61,404,966 image-level labels on 20,638 classes Images are under a CC BY 2.0 license, annotations under CC BY 4.0 license.

VidOR (Video Object Relation) dataset contains 10,000 videos (98.6 hours) from YFCC100M collection together with a large amount of fine-grained annotations for relation understanding. In particular, 80 categories of objects are annotated with bounding-box trajectory to indicate their spatio-temporal location in the videos; and 50 categories of relation predicates are annotated among all pairs of annotated objects with starting and ending frame index. This results in around 50,000 object and 380,000 relation instances annotated. To use the dataset for model development, the dataset is split into 7,000 videos for training, 835 videos for validation, and 2,165 videos for testing.

A dataset containing 5000 images with 37,993 thousand relationships. The dataset contains 100 object categories and 70 predicate categories connecting those objects together. Source: Visual Relationship Detection with Language prior and Softmax

Green family of datasets for emergent communications on relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications.