Visual Genome

Extracts and structures semantic relationships between objects in images using scene graph representations where nodes are objects and edges encode spatial/semantic relationships (e.g., 'person sitting on bench', 'cup on table'). The dataset provides pre-annotated scene graphs for 108K images, enabling models to learn structured reasoning about object interactions rather than treating images as flat feature vectors. Each relationship is labeled with predicate types (spatial: 'on', 'under'; semantic: 'wearing', 'holding') and grounded to pixel coordinates.

Provides 5.4 million natural language descriptions grounded to specific image regions (bounding boxes), enabling training of vision-language models that map text to visual regions. Each region description is manually written by annotators and linked to pixel coordinates, creating a dense supervision signal for learning region-text alignment. Descriptions range from simple object names to complex compositional descriptions capturing attributes, actions, and relationships.

Contains 1.7 million visual question-answer pairs grounded in scene context, where questions reference objects, relationships, and attributes visible in images. Questions are paired with images and scene graphs, enabling models to learn to answer questions by reasoning over visual structure rather than pattern-matching. Answer types range from simple object names to complex compositional answers requiring multi-step reasoning over relationships.

Provides 3.8 million annotated object instances with bounding boxes, class labels, and 2.8 million attribute annotations (e.g., color, material, size, state). Each object is labeled with multiple attributes describing its visual properties, enabling training of models that predict not just object categories but fine-grained visual properties. Attributes are structured as key-value pairs (e.g., 'color: red', 'material: wood') and grounded to specific object instances.

Integrates images, scene graphs, region descriptions, object attributes, and QA pairs into a unified multimodal dataset, enabling end-to-end training of vision-language models that learn from multiple supervision signals simultaneously. The dataset structure allows models to leverage complementary annotations (e.g., region descriptions for grounding, scene graphs for reasoning, attributes for fine-grained understanding) in a single training pipeline. Supports multi-task learning where models jointly optimize for detection, grounding, VQA, and relationship prediction.

Enables indexing and retrieval of images based on scene graph structure and relationships, allowing queries like 'find images with a person sitting on a bench' or 'images where a dog is next to a car'. Scene graphs are indexed as structured knowledge representations, supporting semantic search over visual relationships rather than keyword matching. Retrieval can be performed by querying for specific objects, relationships, or relationship patterns.

Provides statistical analysis and distribution information about visual relationships, objects, and attributes across the dataset, enabling researchers to understand frequency patterns, co-occurrence statistics, and relationship distributions. Includes statistics on predicate frequencies, object co-occurrence patterns, attribute distributions, and relationship types. Enables analysis of visual knowledge biases and patterns in the dataset.

Enables training of compositional visual understanding models by providing structured annotations that decompose images into objects, attributes, and relationships. Models can learn to compose understanding from parts (objects + attributes + relationships) rather than treating images as monolithic wholes. Supports learning of compositional generalization where models understand novel combinations of known objects and relationships.