SGTR+: End-to-end Scene Graph Generation with Transformer (2401.12835v1)

Abstract: Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers from high time complexity or suboptimal designs. In this work, we propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To address the issues above, we create a transformer-based end-to-end framework to generate the entity and entity-aware predicate proposal set, and infer directed edges to form relation triplets. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Based on bipartite graph assembling paradigm, we further propose a new technical design to address the efficacy of entity-aware modeling and optimization stability of graph assembling. Equipped with the enhanced entity-aware design, our method achieves optimal performance and time-complexity. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on three challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. Code is available: https://github.com/Scarecrow0/SGTR

• The paper presents a transformer-based architecture that integrates entity-aware predicate generation with a graph assembling module to build accurate scene graphs.
• The study demonstrates enhanced efficiency and improved mean recall on benchmarks like Visual Genome and OpenImages-V6 by reducing decoder layers.
• The approach paves the way for broader applications in autonomous driving and robotics, suggesting future advancements in scene understanding.

Overview of "SGTR+: End-to-end Scene Graph Generation with Transformer"

The paper "SGTR+: End-to-end Scene Graph Generation with Transformer" introduces an innovative approach to Scene Graph Generation (SGG) using a transformer-based architecture aimed at addressing the inherent challenges of the task, such as the complex prediction space induced by the compositional nature of visual relationships. The authors propose a novel framework, SGTR+, that reformulates scene graph generation as a bipartite graph construction problem, utilizing a transformer to provide an end-to-end solution. This approach effectively combines entity and predicate node generation, along with an advanced graph assembling module, to enhance both the performance and computational efficiency of previous methods.

Key contributions include:

1. Transformer-based Entity-aware Predicate Node Generation: The authors propose a transformer architecture to generate predicate nodes that are entity-aware, which effectively incorporates relevant entity information. This approach is designed to better capture the potential associations between predicates and entities, leveraging a structural query representation.
2. Graph Assembling Module: SGTR+ employs a bipartite graph assembling module that builds the scene graph by inferring directed edges conditioned on entity-aware predicates. This module integrates a learnable embedding mechanism, which facilitates a fully differentiable assembly process, allowing joint optimization with the node generators and improving the robustness and stability of the solution.
3. Efficiency and Generalization Improvements: The authors enhance the efficiency of model training and inference through enhanced structural designs and a reduction in decoder layers by harnessing spatial cues from entity nodes. This results in improvements in time complexity over traditional two-stage approaches.

Quantitative experimental results demonstrate that SGTR+ either surpasses or achieves comparable performance to state-of-the-art methods on several challenging SGG benchmarks, such as Visual Genome, OpenImages-V6, and GQA datasets. Notably, the model achieves significant improvements in the mean recall metric, evidencing its robustness and effectiveness due to entity-aware modeling techniques.

The implications of SGTR+ extend beyond technical advancements by hinting at future prospects:

1. Further Integration of Transformer Models: The successful application of transformer architectures in scene understanding tasks suggests potential advancements for other structured prediction tasks in computer vision.
2. Extensibility to Broader Applications: The approach's capability to efficiently model complex relationships could be extended to fields requiring robust scene understanding, such as autonomous driving and robotics.
3. Potential for Optimization: While offering performance enhancements, SGTR+ opens the door for further research into optimizing transformer models in terms of computational efficiency and scalability for large-scale tasks.

In conclusion, SGTR+ presents a refined and efficient framework for scene graph generation that advances both theoretical and practical comprehension of visual relationships. This paper adds to the growing body of research leveraging transformer models, enhancing our understanding of the complex interplay between entities in visual scenes. This paper could pave the way for future developments across various visual recognition and understanding tasks, showing promise for transformative potential in the field of computer vision.