Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation

Abstract

Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (subject, predicate, object) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE, we propose a context-augmented translation embedding model that can capture both common and rare relations. The previous VTransE model maps entities and predicates into a low-dimensional embedding vector space where the predicate is interpreted as a translation vector between the embedded features of the bounding box regions of the subject and the object. Our model additionally incorporates the contextual information captured by the bounding box of the union of the subject and the object, and learns the embeddings guided by the constraint predicate $\approx$ union (subject, object) $-$ subject $-$ object. In a comprehensive evaluation on multiple challenging benchmarks, our approach outperforms previous translation-based models and comes close to or exceeds the state of the art across a range of settings, from small-scale to large-scale datasets, from common to previously unseen relations. It also achieves promising results for the recently introduced task of scene graph generation.

References in corpus (1)

• Very Deep Convolutional Networks for Large-Scale Image Recognition

Cited by in corpus (10)

• A Comprehensive Survey of Scene Graphs: Generation and Application
• Predicate correlation learning for scene graph generation
• Hierarchical Memory Learning for Fine-Grained Scene Graph Generation
• Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations
• Exploring the Hierarchy in Relation Labels for Scene Graph Generation
• Towards Overcoming False Positives in Visual Relationship Detection
• HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos
• Zero-Shot Scene Graph Generation via Triplet Calibration and Reduction
• Fully Convolutional Scene Graph Generation
• REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter