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.

Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language. Read our paper.

Aug 29, 2016 · Read the Readme Download images part 1 (9.2 GB), part 2 (5.47 GB) Download image meta data (17.62 MB) Download region descriptions (712.07 MB) Download question answers (803.19 MB) Download objects (413.87 MB) Download attributes (462.56 MB) Download relationships (709.58 MB) Download synset name and descriptions (2.20 MB) Download the region graphs (2.78 GB) Download the scene graphs (739.37 ...

Feb 23, 2016 · In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models.

下面的介绍参考李飞飞组发表在IJCV上的论文 Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations。 简单地概括，VG数据集主要由4个部分组成： Region Description：图片被划分成一个个region，每个region都有与其对应的一句自然语言描述。 Region Graph：每个region中的object、attribute、relationship被提取出来，构成局部的“Scene Graph”。 Scene Graph：把一张图片中的所有Region Graph合并成一个全局的Scene Graph。

Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language. Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such …

Vector graphics are a form of computer graphics in which visual images are created directly from geometric shapes defined on a Cartesian plane, such as points, lines, curves and polygons.

Dec 25, 2022 · VG contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. 3 region descriptions and their corresponding region...

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Feb 17, 2019 · Visual Genome 数据集对于 attributes 进行扩展，其 attributes 不是 image-specific 的，而是真实场景中 object-specific 的. attributes 类型包括：size (如 small), pose (如bent), state (如 transparent), emotion (如 happy)等等.