Open-Vocabulary Scene Graph Generation with Vision-Language Models

Core Concepts

Our framework leverages vision-language pre-trained models to generate scene graphs with both known and novel visual relation concepts, outperforming previous methods on open-vocabulary scene graph generation benchmarks.

Abstract

The paper proposes a novel framework for open-vocabulary scene graph generation (SGG) based on sequence generation using vision-language models (VLMs). The key highlights are: The framework formulates SGG as an image-to-sequence generation task, leveraging the strong capabilities of VLMs for open-vocabulary relation modeling. This allows generating scene graphs with both known and novel visual relation concepts. The framework introduces scene graph prompts to transform scene graphs into a sequence representation with relation-aware tokens. A plug-and-play relationship construction module is then used to extract the final scene graph from the generated sequence. The unified image-to-text generation approach facilitates seamless knowledge transfer from the SGG model to downstream vision-language tasks, leading to consistent performance improvements. Extensive experiments on open-vocabulary SGG benchmarks demonstrate the superiority of the proposed framework over previous methods. It also shows the effectiveness of the explicit relational knowledge learned during SGG in enhancing performance on various vision-language tasks.

Stats

The main objective of scene graph generation is to parse an image into a graph representation that describes visual scenes in terms of object entities and their relationships. Most previous SGG methods focus on a limited subset of diverse visual relationships in the real world, resulting in incomplete scene representations. Recent works have started to tackle open-vocabulary SGG by exploiting the image-text matching capability of pre-trained VLMs, but they typically focus on simplified settings or subtasks.

Quotes

"Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks." "To address this challenge, we introduce a new open-vocabulary SGG framework based on sequence generation. Our framework leverages vision-language pre-trained models (VLM) by incorporating an image-to-graph generation paradigm." "By doing so, we harness the strong capabilities of VLM for open-vocabulary SGG and seamlessly integrate explicit relational modeling for enhancing the VL tasks."

Key Insights Distilled From

From Pixels to Graphs

by Rongjie Li,S... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00906.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex scene graph structures, such as hierarchical or nested relationships

To extend the proposed framework to handle more complex scene graph structures like hierarchical or nested relationships, several modifications can be implemented. One approach is to introduce a multi-level scene graph generation process where the initial scene graph is generated at a high level, capturing the primary relationships between entities. Subsequently, this high-level scene graph can serve as input for a secondary refinement step that focuses on capturing more detailed hierarchical or nested relationships. This refinement step can involve additional modules or models that specialize in identifying and representing these complex structures. By incorporating a hierarchical modeling approach, the framework can effectively handle more intricate scene graph structures.

What are the potential limitations of the current approach in terms of scalability or computational efficiency, and how could they be addressed

One potential limitation of the current approach may lie in its scalability and computational efficiency, especially when dealing with large-scale datasets or complex scene graph structures. To address this, optimization techniques such as model parallelism or distributed training can be employed to enhance scalability and reduce training time. Additionally, techniques like knowledge distillation or model compression can be utilized to reduce the computational burden while maintaining performance. Another strategy could involve designing more efficient modules or models within the framework to streamline the computation process. By optimizing the architecture and training procedures, the framework can overcome scalability and efficiency limitations.

Given the success of the framework in enhancing various vision-language tasks, how could the learned relational knowledge be further leveraged to improve other AI systems beyond the scope of this work

The learned relational knowledge from the framework can be further leveraged to enhance various AI systems beyond vision-language tasks. One potential application is in knowledge graph construction, where the relational knowledge can be utilized to improve the accuracy and completeness of knowledge graphs. The learned relationships can also be applied in recommendation systems to enhance personalized recommendations based on intricate user-item relationships. Moreover, in natural language processing tasks, the relational knowledge can aid in improving coreference resolution, entity linking, and semantic parsing. By integrating the learned relational knowledge into these AI systems, overall performance and accuracy can be significantly enhanced.