The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision (1904.12584v1)

Abstract: We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.

Neuro-Symbolic Concept Learner: An Expert Overview

The paper presents the development of the Neuro-Symbolic Concept Learner (NS-CL), a model aiming to integrate visual perception, linguistic parsing, and semantic reasoning without direct supervision. NS-CL achieves this by processing images and paired textual question-answer pairs, constructing a framework that effectively learns visual concepts, word semantics, and sentence structure from natural supervision alone.

Methodology

NS-CL comprises several core components:

1. Visual Perception Module: This is responsible for extracting object-level representations from scenes using Mask R-CNN for object proposals and ResNet-34 for feature extraction. It ensures a contextual understanding of visual scenes essential for reasoning about attributes like size and spatial relations.
2. Semantic Parsing: Utilizing a domain-specific language (DSL) designed for Visual Question Answering (VQA), this module translates textual questions into executable programs. It incorporates an encoder-decoder architecture to process natural language into hierarchical symbolic structures.
3. Symbolic Program Execution: Once the semantic parsing is complete, a quasi-symbolic program execution occurs. It operates on latent object representations, executing parsed programs to provide answers by classifying visual and relational attributes probabilistically.

Results and Claims

NS-CL demonstrates superior accuracy in concept learning and visual reasoning tasks. It achieves state-of-the-art performance on the CLEVR dataset, showcasing robustness in handling object-based representations and precise semantic parsing. The framework is particularly notable for its data efficiency, requiring significantly less training data compared to conventional models.

The paper details extensive experimentation:

• Visual Concept Learning: The model achieves near-perfect classification accuracy across color, shape, and material attributes.
• Data-Efficient Visual Reasoning: Outperforming baseline models, NS-CL maintains high accuracy even when trained on less than 10% of the available data.
• Generalization: It exhibits strong generalization capabilities by transferring learned visual concepts to new attributes, compositions, and even extending to new DSLs such as image-caption retrieval.

Future Directions

The implications of this research are significant in both practical and theoretical contexts:

• Practical Applications: NS-CL can be adapted for real-world visual reasoning tasks, including visual question answering, image retrieval, and potentially domains like robotic manipulation.
• Theoretical Extensions: The framework encourages further exploration into neuro-symbolic integration, potentially advancing the development of models capable of reasoning about dynamic scenes and abstract concepts.

While the NS-CL offers a compelling approach, future work may explore handling more complex natural language interactions, exploring its applicability to video data, and refining its capacity to reason with abstract concepts such as events and actions.

The NS-CL represents a pivotal step towards creating interpretable AI systems capable of multifaceted visual and linguistic reasoning, marking a noteworthy contribution to the field of AI and cognitive modeling.