Scene Graph Generation with External Knowledge and Image Reconstruction (1904.00560v1)

Abstract: Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\etc. However, existing datasets are biased in terms of object and relationship labels, or often come with noisy and missing annotations, which makes the development of a reliable scene graph prediction model very challenging. In this paper, we propose a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome these dataset issues. In particular, we extract commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability in scene graph generation. To address the bias of noisy object annotations, we introduce an auxiliary image reconstruction path to regularize the scene graph generation network. Extensive experiments show that our framework can generate better scene graphs, achieving the state-of-the-art performance on two benchmark datasets: Visual Relationship Detection and Visual Genome datasets.

• The paper introduces a dual strategy combining external commonsense knowledge and image reconstruction loss to mitigate dataset bias and enhance scene graph generation.
• It achieves state-of-the-art performance on VRD and VG benchmarks, significantly improving recall and precision in visual relationship detection.
• The approach employs dynamic memory networks for multi-hop reasoning, enabling more accurate inference of object relationships in complex scenes.

Scene Graph Generation with External Knowledge and Image Reconstruction

This paper introduces a novel approach to scene graph generation by integrating external knowledge and utilizing image reconstruction loss. Scene graph generation is fundamental for higher-level visual understanding tasks. It involves detecting objects and their relationships in images, an endeavor that is often hampered by the limitations inherent in current datasets, such as bias and noise.

The proposed method seeks to mitigate these challenges by using a dual strategy: leveraging external commonsense knowledge to enrich feature representations and introducing an auxiliary image-level regularization path. The model aims to improve the generalizability of scene graph generation networks and achieve more accurate visual relationship detection.

Key Methodological Advances

1. Incorporation of External Knowledge: The paper deploys a novel feature refinement network that uses commonsense knowledge extracted from ConceptNet to supplement visual features. This external knowledge aids the model in dealing with biases and gaps in existing datasets by reinforcing its understanding of object relationships through established world knowledge.
2. Image Reconstruction as Regularization: To address the issue of noisy object annotations, an auxiliary image reconstruction pathway is integrated into the network. This component serves as a regularizer, enforcing that the reconstructed image resembles the original. This approach is particularly effective in enhancing object detection performance, allowing the model to better capture scene structure despite dataset imperfections.
3. Integration with Dynamic Memory Networks (DMN): The external knowledge elements are processed through a DMN, which performs multi-hop reasoning over the retrieved facts, thus enabling more sophisticated inference regarding object relationships.
4. Extensive Experimental Validation: The paper demonstrates that their method achieves state-of-the-art performance on two benchmark datasets, VRD and VG. Their results highlight significant improvements in recall rates for scene graph generation tasks, offering evidence of the robustness of their approach.

Quantitative Results and Implications

The experimental results show significant improvements across key metrics used to evaluate scene graph generation, specifically in detection and recognition tasks. The integration of external knowledge resources led to improvements in precision and recall in generating relationships within images, attesting to the efficacy of including structured commonsense knowledge in training machine learning models for visual understanding.

Furthermore, the model's enhanced object detection accuracy, supported by the auxiliary regularization path, has broader implications for applications reliant on reliable object detection, such as visual question answering and image captioning.

Future Directions

This research opens several avenues for future work. Extending the framework to incorporate other types of external knowledge bases and exploring its application in cross-modal domains can further enhance the versatility of scene graph generation models. Additionally, the integration of real-time capabilities into the framework can elevate its applicability to more dynamic scenarios, like video analysis.

In summary, this paper presents a sophisticated approach that significantly advances the field of scene graph generation. By leveraging external knowledge and employing innovative regularization techniques, the research effectively tackles existing challenges in the domain, setting the stage for promising future developments in AI-driven image understanding.