SGTR: End-to-end Scene Graph Generation with Transformer (2112.12970v3)

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 a point-based one-stage approach, which often suffers from high time complexity or sub-optimal 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 solve the problem, we develop a transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets. In particular, we develop a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships. 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. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on two challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. We hope our model can serve as a strong baseline for the Transformer-based scene graph generation. Code is available: https://github.com/Scarecrow0/SGTR

• The paper introduces a transformer-based method that models scene graph generation as a bipartite graph construction problem, improving both accuracy and efficiency.
• It leverages an entity-aware predicate representation with parallel transformer decoders to robustly generate relational triplets.
• Experimental results on Visual Genome and OpenImages-V6 benchmarks demonstrate significant improvements in mean recall and computational efficiency.

Insights into SGTR: End-to-end Scene Graph Generation with Transformer

The paper "SGTR: End-to-end Scene Graph Generation with Transformer" presents a novel approach to the challenging task of Scene Graph Generation (SGG). The authors introduce a transformer-based method, SGTR, aimed at tackling inefficiencies and limitations present in prior SGG methods. Traditional techniques often rely on bottom-up two-stage or point-based one-stage frameworks, which may incur significant computational overhead or yield sub-optimal designs. In contrast, SGTR is devised as an end-to-end framework that conceptualizes SGG as a bipartite graph construction problem, thus providing a fresh perspective and methodology for the task.

Methodological Overview

The core idea of SGTR is the reformulation of scene graph generation into a bipartite graph building process. This formulation effectively separates entity and predicate generation tasks from the subsequent inference of relational triplets. The method is characterized by three main steps:

1. Entity and Predicate Proposal Generation: Initialization involves generating distinct sets of nodes for entities and predicates. SGTR employs a transformer architecture comprising of CNN+Transformer encoders for feature extraction, and distinct modules for generating entity and predicate nodes. The entity nodes are produced using a method akin to DETR’s object detection framework, while the predicate nodes are evaluated using an entity-aware predicate representation that embeds entity context into each predicate node using three parallel transformer decoders.
2. Graph Assembling: A novel graph assembling module is utilized to infer connections in the bipartite scene graph. This involves predicting directed edges between entity and predicate nodes based on their structural representations.
3. Inference of Relation Triplets: The method yields structured scene graphs by identifying associations between generated nodes, represented as relation triplets comprised of subject entity, predicate, and object entity.

Experimental Results

The authors report robust experimental results demonstrating that SGTR achieves state-of-the-art or comparable performance on challenging benchmarks, notably Visual Genome and OpenImages-V6 datasets. The model not only surpasses many existing approaches but also exhibits efficient performance in terms of inference time.

• Numerical Performance: SGTR achieved notable improvements in both mean recall and recall metrics compared to baseline methods. Notably, on Visual Genome, the SGTR model with resampling strategy showed a significant mean recall increase, proving effective in dealing with class imbalance.
• Efficiency: The approach boasts improved computational efficiency, showing competitive inference times better suited for deployment in real-world applications as compared to many traditional SGG models.

Implications and Future Work

The proposed method carries both practical and theoretical implications. Practically, SGTR’s efficiency and performance make it a suitable candidate for integration into applications requiring robust visual understanding such as image retrieval, visual question answering, and automated image annotation. Theoretically, this work suggests a new direction for scene understanding tasks that leverages compositional properties through transformer-based architectures.

Future research can explore further optimization in transformer architecture specific to SGG and improved strategies for dealing with long-tail distributions in training datasets. Additionally, extending SGTR’s framework could enhance its adaptability to broader contexts within computer vision tasks, such as incorporating multi-modal data or cross-domain generalization.

In summary, the introduction of SGTR signifies a promising evolution in scene graph generation endeavors by adeptly employing a transformer-based approach for efficient scene understanding. This paper not only contributes novel methodologies but also sets a strong foundation for further enhancements in the domain of scene graph generation.