Neural Module Networks (1511.02799v4)

Abstract: Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where is the cat?" This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.

Deep Compositional Question Answering with Neural Module Networks

Overview of the Paper

The paper "Deep Compositional Question Answering with Neural Module Networks" by Andreas, Rohrbach, Darrell, and Klein presents a novel approach to visual question answering (VQA) by leveraging neural module networks (NMNs). This approach marries the representational capacity of deep neural networks with the compositional linguistic structure of natural language questions. The authors detail a method to decompose questions based on their linguistic structure and dynamically construct modular networks that are trained jointly to answer these questions.

Core Contributions

The key contributions of this research include:

1. Neural Module Networks (NMNs): The introduction of NMNs, which compose specialized, jointly-learned neural "modules" to form task-specific deep networks based on the linguistic analysis of questions.
2. Dynamic Network Assembly: A novel mechanism that utilizes a semantic parser to determine the structure and specific modules required to answer each question dynamically.
3. State-of-the-art Results: The paper demonstrates the efficacy of NMNs by achieving state-of-the-art results on the VQA natural image dataset and a newly introduced dataset of complex questions about abstract shapes.

Detailed Analysis

Neural Module Networks

NMNs are constructed from a set of reusable modules, each tailored for a specific subtask such as attention, classification, or measurement. These modules are:

• Attention Modules: Identify regions of interest in the image.
• Re-attention Modules: Transform attentions based on spatial relationships.
• Combination Modules: Merge multiple attentions.
• Classification Modules: Produce output labels based on attentions.
• Measurement Modules: Evaluate properties of attentions.

Each module operates in a composable fashion, allowing the system to adapt the network structure to the specific demands of the question.

Dynamic Network Layout

The process begins with parsing the natural language question using the Stanford Parser to extract semantic dependencies, which are then used to generate a structured query. This query determines the network layout, specifying which modules to use and how they should interconnect. The layout is designed to reflect the compositional nature of the question, ensuring that the network can answer questions with varying complexity.

Experimental Validation

Synthetic Shapes Dataset

The authors introduce a new dataset with questions involving spatial relations, set-theoretic reasoning, and attribute recognition applied to synthetic images of shapes. NMNs significantly outperform a VIS+LSTM baseline, particularly on questions requiring complex reasoning. The model achieves up to 25% higher accuracy compared to competitors.

VQA Dataset

On the VQA natural image dataset, NMNs combined with a sequence model (LSTM) outperform previous methods, achieving superior results especially on object, attribute, and number-based questions. The system excels at modeling compositional structures that are often present in natural language queries.

Implications and Future Directions

The implications of this research extend beyond visual question answering. The modular framework of NMNs offers a generalizable method for dynamic task-specific network construction. This approach could be applied to a variety of domains, including text-based question answering and signal processing tasks. Future research could explore joint learning of semantic parsers and module parameters, integrate better parsing techniques to reduce errors, and expand the library of reusable neural modules.

Conclusion

This paper presents a significant stride in the development of specialized, dynamically constructed neural networks for question answering. By leveraging the compositional nature of language and the adaptability of neural networks, NMNs show promise in handling complex, multi-faceted queries more effectively than monolithic models. This work lays the groundwork for more sophisticated, generalizable neural architectures capable of complex reasoning across diverse applications.