Visual Relationship Detection
36 papers with code • 5 benchmarks • 5 datasets
Visual relationship detection (VRD) is one newly developed computer vision task aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition and is essential for fully understanding images, even the visual world.
Benchmarks
These leaderboards are used to track progress in Visual Relationship Detection
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Visual Genome
VRD
Open Images V7
Visual Relationship Detection Dataset
TexRel
Latest papers with no code
Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection
We provide a single-stage recipe to train this model on a mixture of object and relationship detection data.
RelVAE: Generative Pretraining for few-shot Visual Relationship Detection
Visual relations are complex, multimodal concepts that play an important role in the way humans perceive the world.
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
NeSy4VRD is a multifaceted resource designed to support the development of neurosymbolic AI (NeSy) research.
Image Semantic Relation Generation
Scene graphs provide structured semantic understanding beyond images.
Learning Structured Representations of Visual Scenes
As the intermediate-level representations bridging the two levels, structured representations of visual scenes, such as visual relationships between pairwise objects, have been shown to not only benefit compositional models in learning to reason along with the structures but provide higher interpretability for model decisions.
VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection
Visual Relationship Detection (VRD) impels a computer vision model to 'see' beyond an individual object instance and 'understand' how different objects in a scene are related.
Scene Graph Generation: A Comprehensive Survey
In this paper, we provide a comprehensive survey of recent achievements in this field brought about by deep learning techniques.
A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection
In particular, BPGR can also provide easy-to-understand insights for reasoning results to show interpretability.
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation
Scene graph generation (SGG) aims to identify the objects and their relationships.
Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries
Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both the parts (subject, object, and predicate) and the sum (triplet as a whole) are to be detected in a hierarchical fashion.
