{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"visual-genome","slug":"visual-genome","name":"Visual Genome","type":"dataset","url":"https://homes.cs.washington.edu/~ranjay/visualgenome/","page_url":"https://unfragile.ai/visual-genome","categories":["model-training","image-generation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"visual-genome__cap_0","uri":"capability://data.processing.analysis.scene.graph.based.visual.relationship.extraction","name":"scene-graph-based visual relationship extraction","description":"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.","intents":["Train vision models that understand spatial and semantic relationships between objects, not just object detection","Build visual reasoning systems that can answer 'what is the relationship between X and Y' queries","Create scene understanding models that generate structured knowledge representations from images","Develop visual grounding systems that map language descriptions to specific object pairs and their relationships"],"best_for":["Computer vision researchers building scene understanding models","Teams developing visual reasoning and VQA systems","ML engineers training multimodal models requiring structured visual knowledge"],"limitations":["Scene graphs are manually annotated, introducing subjective bias in relationship definitions and predicate selection","Predicate vocabulary is limited to ~100 relationship types, may not capture domain-specific relationships","Annotation coverage is uneven — some images have dense relationship annotations while others are sparse","Relationships are binary (between two objects) — does not capture n-ary relationships or complex spatial configurations"],"requires":["Image files (JPEG/PNG format)","Scene graph JSON files with node/edge structure","Graph processing library (NetworkX, PyTorch Geometric, or DGL)","Python 3.6+ for dataset loading and processing"],"input_types":["image files (JPEG, PNG)","scene graph JSON (nodes: objects with attributes; edges: relationships with predicates)","region bounding boxes (x, y, width, height coordinates)"],"output_types":["structured scene graphs (node-edge representations)","relationship triplets (subject, predicate, object)","spatial relationship vectors","semantic relationship embeddings"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_1","uri":"capability://data.processing.analysis.dense.region.description.grounding","name":"dense-region-description-grounding","description":"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.","intents":["Train vision-language models that can ground natural language phrases to image regions","Build visual question answering systems that understand region-level semantics","Create image captioning models that generate region-specific descriptions","Develop visual search systems that match text queries to image regions"],"best_for":["Researchers building vision-language foundation models (CLIP-style architectures)","Teams developing region-based visual understanding systems","ML engineers training dense visual grounding models"],"limitations":["Region descriptions are subjective and vary in length/detail across annotators, introducing inconsistency","Regions are rectangular bounding boxes, not semantic segmentation masks — cannot capture non-rectangular objects","Description vocabulary and style vary significantly, making it harder to learn consistent text-region mappings","Regions may overlap or contain multiple objects, creating ambiguity in region-text alignment"],"requires":["Image files (JPEG/PNG)","Region annotation JSON with bounding box coordinates and text descriptions","Text tokenizer (BERT, GPT-style) for encoding descriptions","Vision encoder (ResNet, ViT) for image feature extraction"],"input_types":["image files (JPEG, PNG)","region bounding boxes (x, y, width, height)","natural language descriptions (free-form text, 5-50 words typical)"],"output_types":["region-text alignment scores","region embeddings","text embeddings","grounding predictions (region coordinates for given text)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_2","uri":"capability://data.processing.analysis.visual.question.answering.dataset.with.scene.context","name":"visual-question-answering-dataset-with-scene-context","description":"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.","intents":["Train visual question answering models that reason over scene structure and object relationships","Build VQA systems that can answer questions about spatial relationships, attributes, and object interactions","Create visual reasoning models that perform multi-hop reasoning (e.g., 'what is the person holding that is on the table')","Develop evaluation benchmarks for visual understanding and reasoning capabilities"],"best_for":["Researchers developing visual reasoning and VQA models","Teams building multimodal AI systems requiring visual understanding","ML engineers evaluating vision-language model capabilities"],"limitations":["Questions are biased toward objects and relationships present in scene graphs, may not cover all visual phenomena","Answer distribution is imbalanced — some answers appear frequently while others are rare, affecting model training","Questions are English-only, limiting multilingual VQA research","Question complexity varies widely, making it difficult to evaluate specific reasoning capabilities in isolation"],"requires":["Image files (JPEG/PNG)","Question-answer JSON pairs with image IDs","Scene graph annotations for reasoning context","VQA evaluation metrics (accuracy, BLEU, METEOR, CIDEr)"],"input_types":["image files (JPEG, PNG)","natural language questions (free-form text, 5-20 words typical)","scene graph context (optional, for structured reasoning)"],"output_types":["answer text (single word or short phrase typical)","answer confidence scores","reasoning traces (if model provides explanations)","VQA evaluation metrics"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_3","uri":"capability://data.processing.analysis.object.instance.detection.with.dense.attributes","name":"object-instance-detection-with-dense-attributes","description":"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.","intents":["Train object detection models that predict both category and visual attributes","Build attribute prediction systems that understand fine-grained visual properties","Create product recognition systems that identify objects and their visual characteristics","Develop visual search systems that filter by object attributes"],"best_for":["Computer vision teams building attribute-aware detection models","E-commerce platforms developing product recognition with attribute extraction","Researchers studying fine-grained visual understanding"],"limitations":["Attribute vocabulary is limited and may not cover domain-specific properties","Attribute annotations are incomplete — not all objects have all attributes annotated","Attribute definitions are subjective (e.g., 'large' vs 'small' depends on context)","Bounding boxes are axis-aligned rectangles, cannot capture rotated or non-rectangular objects"],"requires":["Image files (JPEG/PNG)","Object instance annotations (bounding boxes, class labels)","Attribute annotations (key-value pairs)","Object detection framework (YOLO, Faster R-CNN, RetinaNet)"],"input_types":["image files (JPEG, PNG)","object bounding boxes (x, y, width, height)","object class labels","attribute key-value pairs"],"output_types":["object detections (bounding boxes + class labels)","attribute predictions (key-value pairs per object)","attribute confidence scores","detection metrics (mAP, precision, recall)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_4","uri":"capability://data.processing.analysis.multimodal.dataset.integration.for.vision.language.models","name":"multimodal-dataset-integration-for-vision-language-models","description":"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.","intents":["Train unified vision-language models that leverage multiple supervision signals","Build multi-task learning systems that jointly optimize for detection, grounding, and reasoning","Create foundation models that learn rich visual understanding from diverse annotations","Develop transfer learning pipelines that leverage pre-training on multiple tasks"],"best_for":["Research teams developing foundation vision-language models","ML engineers building multi-task learning systems","Teams training models that require diverse visual understanding capabilities"],"limitations":["Integrating multiple annotation types requires careful data alignment and deduplication","Different annotation types have different quality levels and coverage, creating imbalanced training signals","Multi-task learning adds complexity to model architecture and training procedures","Computational requirements are high due to large dataset size and multi-task objectives"],"requires":["Image files (JPEG/PNG)","Scene graph JSON annotations","Region description annotations","Object instance and attribute annotations","QA pair annotations","Multi-task learning framework (PyTorch, TensorFlow)"],"input_types":["image files (JPEG, PNG)","scene graphs (nodes, edges, predicates)","region descriptions (text + bounding boxes)","object instances (bounding boxes, classes, attributes)","question-answer pairs"],"output_types":["multi-task predictions (detections, attributes, relationships, answers)","unified visual embeddings","task-specific outputs (per task)","multi-task loss values"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_5","uri":"capability://search.retrieval.scene.graph.based.image.retrieval.and.indexing","name":"scene-graph-based-image-retrieval-and-indexing","description":"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.","intents":["Build image search systems that query by visual relationships and scene structure","Create visual knowledge bases indexed by scene graphs for efficient retrieval","Develop systems that find images matching complex spatial/semantic relationship queries","Enable research on visual relationship understanding through large-scale retrieval"],"best_for":["Teams building visual search engines with relationship-based queries","Researchers studying visual relationship distributions and patterns","ML engineers developing scene understanding systems requiring large-scale retrieval"],"limitations":["Scene graph indexing requires pre-computed annotations, cannot retrieve images with novel relationships not in training set","Query formulation requires understanding scene graph structure and predicate vocabulary","Retrieval performance depends on annotation quality and completeness","Scalability challenges when indexing millions of scene graphs with complex relationship patterns"],"requires":["Pre-computed scene graph annotations for all images","Graph database or search index (Neo4j, Elasticsearch with custom indexing)","Query parser for scene graph query language","Python 3.6+ with graph processing libraries"],"input_types":["scene graph queries (structured or natural language)","relationship patterns (subject-predicate-object triplets)","object class filters","attribute filters"],"output_types":["ranked list of image IDs","matching scene subgraphs","relationship match scores","retrieval metrics (precision, recall, MRR)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_6","uri":"capability://data.processing.analysis.visual.relationship.distribution.analysis.and.statistics","name":"visual-relationship-distribution-analysis-and-statistics","description":"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.","intents":["Analyze visual relationship distributions and patterns in large-scale image datasets","Understand object co-occurrence and relationship frequency patterns","Identify dataset biases and imbalances in relationship annotations","Evaluate model performance against relationship frequency baselines"],"best_for":["Researchers studying visual relationship distributions and patterns","Teams analyzing dataset biases and annotation quality","ML engineers developing balanced training strategies for relationship prediction"],"limitations":["Statistics are specific to Visual Genome and may not generalize to other datasets","Frequency-based analysis can reinforce dataset biases rather than address them","Long-tail relationships have sparse statistics, making analysis unreliable","Statistical patterns may not reflect real-world visual relationship distributions"],"requires":["Scene graph annotations with relationship labels","Object and attribute annotations","Statistical analysis tools (pandas, numpy, scipy)","Visualization libraries (matplotlib, seaborn)"],"input_types":["scene graph annotations","object instance annotations","attribute annotations"],"output_types":["relationship frequency distributions","object co-occurrence matrices","attribute frequency distributions","statistical summaries (mean, std, percentiles)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"visual-genome__cap_7","uri":"capability://data.processing.analysis.compositional.visual.understanding.through.structured.annotations","name":"compositional-visual-understanding-through-structured-annotations","description":"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.","intents":["Train compositional visual understanding models that generalize to novel object combinations","Build models that understand images through object-attribute-relationship decomposition","Create systems that can reason about unseen visual combinations of known components","Develop models that explain visual understanding through structured decompositions"],"best_for":["Researchers studying compositional generalization in vision","Teams building explainable visual understanding systems","ML engineers developing models that reason about visual structure"],"limitations":["Compositional understanding requires careful model architecture design, not automatic from data","Annotations may not capture all compositional factors (e.g., spatial arrangements, occlusions)","Compositional generalization to truly novel combinations is limited by training distribution","Evaluating compositional understanding requires specialized benchmarks beyond standard metrics"],"requires":["Scene graph annotations with explicit object, attribute, and relationship labels","Compositional model architecture (e.g., neural-symbolic, graph neural networks)","Evaluation benchmarks for compositional generalization","Python 3.6+ with deep learning frameworks"],"input_types":["scene graphs (objects, attributes, relationships)","object instances with attributes","relationship annotations"],"output_types":["compositional predictions (object + attribute + relationship)","compositional embeddings","generalization metrics (accuracy on novel combinations)","interpretable decompositions"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Image files (JPEG/PNG format)","Scene graph JSON files with node/edge structure","Graph processing library (NetworkX, PyTorch Geometric, or DGL)","Python 3.6+ for dataset loading and processing","Image files (JPEG/PNG)","Region annotation JSON with bounding box coordinates and text descriptions","Text tokenizer (BERT, GPT-style) for encoding descriptions","Vision encoder (ResNet, ViT) for image feature extraction","Question-answer JSON pairs with image IDs","Scene graph annotations for reasoning context"],"failure_modes":["Scene graphs are manually annotated, introducing subjective bias in relationship definitions and predicate selection","Predicate vocabulary is limited to ~100 relationship types, may not capture domain-specific relationships","Annotation coverage is uneven — some images have dense relationship annotations while others are sparse","Relationships are binary (between two objects) — does not capture n-ary relationships or complex spatial configurations","Region descriptions are subjective and vary in length/detail across annotators, introducing inconsistency","Regions are rectangular bounding boxes, not semantic segmentation masks — cannot capture non-rectangular objects","Description vocabulary and style vary significantly, making it harder to learn consistent text-region mappings","Regions may overlap or contain multiple objects, creating ambiguity in region-text alignment","Questions are biased toward objects and relationships present in scene graphs, may not cover all visual phenomena","Answer distribution is imbalanced — some answers appear frequently while others are rare, affecting model training","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-05T11:48:35.331Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=visual-genome","compare_url":"https://unfragile.ai/compare?artifact=visual-genome"}},"signature":"JbEvlHh6ieTrUzTi66krvm+pUknArjjB8O82TIZ5xa0Y/xslZe2lgK6M6OJ/ozpa+sxVfYdgt0Wo8SjHHpexDQ==","signedAt":"2026-06-21T02:21:22.924Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/visual-genome","artifact":"https://unfragile.ai/visual-genome","verify":"https://unfragile.ai/api/v1/verify?slug=visual-genome","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}