Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding (1810.02338v2)

Abstract: We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three unique advantages. First, executing programs on a symbolic space is more robust to long program traces; our model can solve complex reasoning tasks better, achieving an accuracy of 99.8% on the CLEVR dataset. Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step.

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

The paper "Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding" presents an innovative approach that combines neural networks and symbolic reasoning to address challenges in visual question answering (VQA). This neural-symbolic VQA (NS-VQA) system effectively separates the reasoning process from visual perception and language understanding, leveraging the robustness and interpretability of symbolic methods alongside the flexibility of deep learning.

Core Approach

The NS-VQA model consists of three main components: a scene parser (de-renderer), a question parser, and a program executor. The scene parser uses neural networks to generate a structural representation of the scene, segmenting the image and recognizing object attributes with the help of Mask R-CNN and ResNet-34. The question parser maps natural language questions to programs through a sequence-to-sequence model with a bidirectional LSTM, enabling it to capture the hierarchical structure of the queries. Finally, the program executor interprets the program on the symbolic scene representation to derive an answer.

Advantages and Results

The key advantages of incorporating symbolic structures include:

1. Robustness and Efficiency: On the CLEVR dataset, NS-VQA achieves an outstanding accuracy of 99.8%, demonstrating its capability to handle complex reasoning tasks with minimal data and computational resources. The method outperformed state-of-the-art models and showed high data efficiency—requiring only a small number of annotated programs and question-answer pairs.
2. Interpretability: With fully transparent symbolic reasoning, the process can be analyzed and understood step-by-step, allowing for better debugging and comprehension of the model's decisions.
3. Generalization: NS-VQA generalizes well across different question styles (e.g., CLEVR-Humans) and novel attribute combinations in datasets such as CLEVR-CoGenT. The disentangled architecture helps in adapting the model to new tasks with minimal fine-tuning.

The paper provides compelling quantitative results, showing NS-VQA's superiority over existing models in various settings. The experiments highlight the system's capability to learn efficiently from limited data while maintaining interpretability.

Implications and Future Directions

The neural-symbolic approach demonstrated in this paper suggests a promising path for integrating deep representation learning with symbolic execution in AI. This integration addresses existing challenges in VQA, like the need for explainability and generalization, and can potentially extend to other AI domains.

Future research could explore incorporating unsupervised or weakly supervised learning techniques to enhance generalization to truly novel situations. Expanding this approach to more complex scenes and real-world data, as well as improving the robustness of scene parsing on real images, will be crucial for practical applications.

In conclusion, the NS-VQA model provides a significant step towards achieving interpretable and efficient visual reasoning by disentangling perception from reasoning via neural-symbolic integration. This paper contributes to the ongoing discourse on balancing learning with innate symbolic reasoning capabilities within AI systems.