Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Modeling relationships between objects (subject, predicate, object) is crucial for image understanding.
• Existing methods struggle to generalize to unseen relations, limiting their real-world applicability.

Purpose of the Study:

• To develop a novel context-augmented translation embedding model for improved visual relation recognition.
• To enhance the generalization capability of models to both common and rare, unseen relations.

Main Methods:

• Proposed a context-augmented translation embedding model inspired by VTransE.
• Incorporated contextual information from the union of subject and object bounding boxes.
• Learned embeddings using the constraint: predicate ≈ union(subject, object) - subject - object.

Main Results:

• The proposed model outperforms previous translation-based models on various benchmarks.
• Achieved state-of-the-art or near state-of-the-art performance across different dataset scales.
• Demonstrated strong results in recognizing both common and unseen visual relations.

Conclusions:

• The context-augmented translation embedding model offers superior performance in visual relation understanding.
• The approach shows significant potential for advancing scene graph generation tasks.