Scene Graph Generation from Objects, Phrases and Region Captions (1707.09700v2)

Abstract: Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generated on top of objects detected in an image with their pairwise relationship predicted, while region captioning gives a language description of the objects, their attributes, relations, and other context information. In this work, to leverage the mutual connections across semantic levels, we propose a novel neural network model, termed as Multi-level Scene Description Network (denoted as MSDN), to solve the three vision tasks jointly in an end-to-end manner. Objects, phrases, and caption regions are first aligned with a dynamic graph based on their spatial and semantic connections. Then a feature refining structure is used to pass messages across the three levels of semantic tasks through the graph. We benchmark the learned model on three tasks, and show the joint learning across three tasks with our proposed method can bring mutual improvements over previous models. Particularly, on the scene graph generation task, our proposed method outperforms the state-of-art method with more than 3% margin.

• The paper proposes the MSDN framework that integrates object detection, scene graph generation, and region captioning for unified scene understanding.
• It employs a dynamic graph to refine features by aligning object, phrase, and caption regions via spatial and semantic connections.
• The model demonstrates a 3.63%-4.31% improvement on Visual Genome tasks, underscoring its potential in advancing joint task learning.

Scene Graph Generation from Objects, Phrases, and Region Captions

The paper "Scene Graph Generation from Objects, Phrases and Region Captions" introduces a novel approach to the integration of three key visual scene understanding tasks: object detection, scene graph generation, and region captioning. The authors propose the Multi-level Scene Description Network (MSDN), a neural network model designed to perform these tasks jointly, leveraging their interconnected nature.

Methodology

The MSDN framework integrates these tasks by aligning object, phrase, and caption regions using a dynamically constructed graph based on spatial and semantic connections. This model facilitates the flow of information between tasks, improving each one's performance through feature refinement.

1. Region Proposal: The model generates three types of region proposals—objects, phrases, and captions. Object proposals are generated using a Region Proposal Network (RPN). Phrase regions are created by pairing object proposals, and caption regions are generated through a separate RPN.
2. Dynamic Graph Construction: The model constructs a graph with nodes representing region features and edges depicting semantic and spatial relationships. The graph's structure dynamically adapts to different images based on detected regions' interactions.
3. Feature Refining: Features of objects, phrases, and captions are refined by passing messages along the edges of the dynamic graph. This process enhances the features by integrating complementary information from interconnected nodes.

Experimental Evaluation

The authors evaluate the MSDN on the Visual Genome dataset, focusing on three sub-tasks: Predicate Recognition (PredCls), Phrase Recognition (PhrCls), and Scene Graph Generation (SGGen). The results demonstrate significant improvements over previous state-of-the-art methods, particularly achieving a 3.63% to 4.31% improvement in scene graph generation.

Results and Contributions

1. Performance: The proposed model considerably outperforms existing methods by leveraging the interconnections between different semantic levels. Notably, the joint learning approach yields notable gains in recall metrics across tasks.
2. Model Innovation: By constructing a dynamic graph and enabling message passing, the MSDN aligns multi-level scene descriptions, enhancing the synergistic effect between the tasks.
3. Future Implications: This approach opens avenues for further research in joint task learning, suggesting that models benefiting from multi-task integration could significantly advance visual scene understanding.

Conclusion

The MSDN provides a robust framework for unified scene understanding tasks, exemplifying how joint task optimization can enhance computational efficiency and accuracy. The paper lays foundational work for future advancements in neural network models capable of performing complex, interconnected visual tasks concurrently. The publicly available code further facilitates ongoing research in this area, encouraging experimentation and the development of improved methods in scene understanding.