Scene Graph Generation by Iterative Message Passing (1701.02426v2)

Abstract: Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. The model solves the scene graph inference problem using standard RNNs and learns to iteratively improves its predictions via message passing. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods for generating scene graphs using Visual Genome dataset and inferring support relations with NYU Depth v2 dataset.

Scene Graph Generation by Iterative Message Passing

The paper "Scene Graph Generation by Iterative Message Passing" by Danfei Xu, Yuke Zhu, Christopher B. Choy, and Li Fei-Fei presents an end-to-end model for generating visually-grounded scene graphs from input images. This approach addresses a fundamental challenge in computer vision: understanding the contextual relationships between objects within a scene.

Overview and Problem Statement

The central thesis of this research is the necessity to move beyond recognizing individual objects in isolation and to model relationships among objects to gain a richer semantic understanding of visual scenes. Existing state-of-the-art perceptual models, such as those based on Faster R-CNN, primarily focus on object detection and recognition without taking object interactions into account. This limitation results in difficulties in distinguishing between semantically different scenes that contain similar objects.

The authors propose the construction of a scene graph—a graph where nodes represent objects and edges depict relationships between these objects. They introduce an innovative model based on recurrent neural networks (RNNs) that facilitates the generation of these scene graphs. By adopting an iterative message-passing mechanism, the model jointly infers object classes, bounding box adjustments, and relationship predicates, using context to refine predictions iteratively.

Model Architecture

The model presented by Xu et al. uses a novel graph inference mechanism that iteratively passes messages between node and edge GRUs, treating the scene graph as a bipartite graph. This process allows the model to use contextual information effectively to improve prediction accuracy. The key components of the model include:

• Node and Edge GRUs: Separate GRU units for nodes and edges which share parameters among all nodes and edges, respectively.
• Message Passing Framework: Messages carry information between nodes and edges to iteratively refine predictions.
• Message Pooling Function: Adaptive weighted sum pooling that dynamically aggregates information from connected nodes and edges.

Datasets and Evaluation Metrics

The model is evaluated on two datasets: a new, cleaned-up subset of the Visual Genome dataset, and the NYU Depth v2 dataset. The experiment setup covers three tasks:

1. Predicate Classification (PredCls): Classifying relationships between a set of given object bounding boxes.
2. Scene Graph Classification (SGCls): Predicting object categories and their relationships given localized objects.
3. Scene Graph Generation (SGGen): Simultaneously detecting objects and predicting relationships, considering an object as correctly detected if its IoU with the ground-truth box is at least 0.5.

Metrics for evaluation include Recall@50 and Recall@100, owing to the sparsity of relationship annotations in the Visual Genome dataset.

Experimental Results

The model significantly outperforms the baseline relationship detection model by Lu et al. (2016), particularly in predicate classification tasks. Key observations include:

• The final model achieves substantial improvements in Recall@100 metrics for predicate classification (53.08%), scene graph classification (24.38%), and scene graph generation (4.24%).
• The proposed incorporation of iterative message passing demonstrated that the contextual learning process enhances the relationship prediction in scene graphs.
• The model also generalizes effectively to other structured prediction problems, outperforming state-of-the-art methods on the support relation inference task on the NYU Depth v2 dataset.

Implications and Future Directions

The implications of this research are profound for both practical applications and theoretical advancements in computer vision. Practically, the generated scene graphs enhance higher-level tasks such as image retrieval, 3D scene understanding, and visual question answering, providing a structured representation that captures rich semantics.

Theoretically, the iterative message-passing framework introduces a scalable and efficient approach to structured prediction problems, utilizing graph-based RNN models for improved contextual inference. This opens avenues for exploring similar principles in other domains and complex vision tasks, potentially integrating more sophisticated context-aware mechanisms and addressing other forms of relationships and higher-order interactions.

Conclusion

The paper presents an advanced method for generating scene graphs, demonstrating significant improvements in visual relationship understanding by leveraging a novel iterative message-passing approach. The empirical results affirm the model's efficacy in both sparse and dense relationship settings, underscoring its versatility and potential to impact a wide range of computer vision tasks.

Overall, the introduction of a primal-dual graph inference framework marks a critical step towards more accurate and contextually-aware scene understanding in computer vision.