What Makes a Maze Look Like a Maze? (2409.08202v2)

Abstract: A unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-LLMs excel at making literal interpretations of images (e.g., recognizing object categories such as tree branches), they still struggle to make sense of such visual abstractions (e.g., how an arrangement of tree branches may form the walls of a maze). To address this challenge, we introduce Deep Schema Grounding (DSG), a framework that leverages explicit structured representations of visual abstractions for grounding and reasoning. At the core of DSG are schemas--dependency graph descriptions of abstract concepts that decompose them into more primitive-level symbols. DSG uses LLMs to extract schemas, then hierarchically grounds concrete to abstract components of the schema onto images with vision-LLMs. The grounded schema is used to augment visual abstraction understanding. We systematically evaluate DSG and different methods in reasoning on our new Visual Abstractions Dataset, which consists of diverse, real-world images of abstract concepts and corresponding question-answer pairs labeled by humans. We show that DSG significantly improves the abstract visual reasoning performance of vision-LLMs, and is a step toward human-aligned understanding of visual abstractions.

• The paper introduces DSG, a framework that decomposes abstract visual concepts into hierarchical schemas for enhanced reasoning.
• The paper demonstrates significant performance improvements, including an 8.3% relative gain in overall accuracy on visual reasoning tasks.
• The paper leverages the Visual Abstractions Dataset (VAD) to benchmark and advance human-level interpretation of complex abstract images.

Deep Schema Grounding: A Framework for Visual Abstraction Understanding

The paper "What Makes a Maze Look Like a Maze?" introduces Deep Schema Grounding (DSG), a novel framework designed to enhance the interpretation of abstract concepts in visual data. This framework aims to address the limitations of current vision-LLMs (VLMs) in reasoning about visual abstractions, advancing towards human-level understanding in AI.

Overview of DSG

Deep Schema Grounding leverages large pre-trained LLMs and vision-LLMs (VLMs) to explicitly extract and ground schemas of abstract concepts. The central component of DSG is the schema—a dependency graph that decomposes high-level abstract concepts into primitive-level symbols. The process involves three main stages:

1. Schema Extraction: LLMs generate schemas that outline the dependency graph of a given abstract concept.
2. Hierarchical Grounding: The VLMs then hierarchically ground these schemas onto images, starting from the most concrete symbols to more abstract ones.
3. Augmented Visual Question-Answering: The grounded schemas are incorporated into the VLMs for enhanced visual reasoning and question-answering tasks.

Contributions and Methodology

The paper makes several critical contributions:

• Introduction of DSG, which adopts a hierarchical decomposition approach to ground schemas of abstract concepts.
• The Visual Abstractions Dataset (VAD), a benchmark consisting of diverse, real-world images paired with questions that probe the understanding of abstract concepts.
• Demonstration of significant improvements in visual abstraction reasoning with DSG across different VLMs, particularly in complex tasks involving spatial relations and counting.

Schema Construction and Grounding

Schemas are extracted from LLMs using a minimalistic prompt, emphasizing universality rather than instance-specific details. For example, the concept "maze" might be decomposed into components such as layout, walls, and entry-exit configurations. This abstraction allows DSG to generalize across various visual instances of the concept.

The grounding process is hierarchical: DSG first resolves concrete components (e.g., the layout of a maze) before tackling more abstract components (e.g., the entry-exit points). This hierarchical grounding is essential for maintaining coherence and ensuring accurate interpretation of complex visual abstractions.

Visual Abstractions Dataset

VAD spans 12 abstract concepts, categorized into strategic, scientific, social, and domestic abstractions. Each image in the dataset is paired with three types of questions: binary-choice, counting, and open-ended. The dataset allows for a comprehensive evaluation of a model's capability to reason about abstract visual concepts.

Numerical Results and Performance Evaluation

DSG demonstrated significant performance improvements over baseline VLMs:

• Overall Accuracy: DSG enhanced GPT-4V's performance by 5.4 percentage points, an 8.3% relative improvement.
• Counting Questions: Notably, DSG achieved a 6.7 percentage point improvement in counting questions, a relative improvement of 11.0%.
• Concept Categories: DSG outperformed GPT-4V across all categories, with the most substantial gains seen in strategic and scientific concepts.

Implications and Future Directions

The DSG framework sets a precedent for structured thinking in visual reasoning systems. By explicitly modeling the underlying schema of abstract concepts, DSG facilitates a more holistic understanding of visual data, akin to human cognition.

Future developments could explore:

• Enhanced Spatial Understanding: Improving the grounding of spatially complex schema components.
• Bias Mitigation: Addressing potential biases in LLM-derived schemas.
• Schema Flexibility: Further refining the flexibility and expressiveness of schema representations to cover a broader range of abstract concepts.

Conclusion

The DSG framework represents a significant step forward in the quest for human-aligned understanding of visual abstractions. By combining the strengths of LLMs and VLMs, DSG offers a promising approach to tackling the complexities inherent in visual abstraction reasoning. The introduction of the Visual Abstractions Dataset further enables systematic evaluation and benchmarking, ultimately contributing to the advancement of AI's cognitive capabilities.