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Chain-of-Sketch: Enabling Global Visual Reasoning (2410.08165v2)

Published 10 Oct 2024 in cs.LG and cs.CV

Abstract: Modern vision models have achieved remarkable success in benchmarks where local features provide critical information about the target. There is now a growing interest in tackling tasks requiring more global reasoning, where local features do not provide significant information. Minsky and Papert put forward such tasks in 1969 with their connectivity study, exposing the limitations of the perceptron model. In this paper, we introduce an expanded set of global visual datasets involving graphs, strings, mazes, and image grids. We show that large vision models still struggle to learn these tasks efficiently. Similarly, state-of-the-art multi-modal LLMs perform poorly on these datasets. We explain this learning inefficiency by means of the 'globality degree' measure. To mitigate this, we propose a method called chain-of-sketch (CoS). Similar to the chain-of-thought and scratchpad techniques used in LLMs, CoS breaks the original task into intermediate visual steps to help learn a complex task. In addition, we show that not all CoS strategies perform equally well. Our key insight is to impose a Markovian structure on the CoS frames. This leads to the introduction of 'inductive CoS' which achieves better out-of-distribution generalization and performs well even with smaller models compared to non-inductive variants.

Summary

  • The paper introduces visual scratchpads that decompose complex visual tasks into step-by-step reasoning sequences.
  • It establishes a novel globality degree metric to quantify task complexity by measuring reliance on dispersed input features.
  • The study demonstrates improved out-of-distribution generalization and staircase learning, enabling efficient global reasoning in vision systems.

Overview of the Paper: Visual Scratchpads: Enabling Global Reasoning in Vision

This paper addresses a critical gap in current vision models by introducing the concept of "visual scratchpads" to enhance global reasoning capabilities. Traditional vision models excel in tasks where local features are pivotal but are challenged by tasks requiring holistic understanding—a necessity for multi-step, global reasoning. The proposed visual scratchpads partition these complex problems into more manageable sub-tasks, analogous to textual scratchpads employed in LLMs.

Key Contributions

  1. Introduction of Global Vision Benchmarks

The paper revisits the limitations of early AI models, akin to Minsky and Papert's experiments, by proposing four global visual benchmarks centered on pathfinding and mazes. These tasks necessitate understanding the entirety of a visual scene, moving beyond reliance on localized features.

  1. Concept of Globality Degree

To elucidate why modern vision models struggle with global tasks, the authors introduce the notion of "globality degree." This concept measures a task’s reliance on multiple, scattered input features to deliver significant information. The higher the degree, the more challenging it becomes for models to resolve, focusing primarily on computational efficiency.

  1. Introduction of Visual Scratchpads

Visual scratchpads assist in transforming global tasks into simpler sub-components. These feature a series of frames that visually depict reasoning steps, inspired by scratchpads used in textual data domains. The paper shows that visual scratchpads markedly improve task learnability, especially when model sizes are limited.

  1. Development of Inductive Scratchpads

The authors propose "inductive scratchpads" for enhanced out-of-distribution (OOD) generalization and efficiency. This recurrent model approach allows for dynamic computation at inference time, progressively generating reasoning steps for complex problem-solving.

Results and Implications

  • The introduction of visual scratchpads significantly boosts performance in global reasoning tasks when compared to baseline methods, which lack these representations.
  • Inductive scratchpads excel in OOD scenarios, outperforming single-frame models by effectively adapting to increased task complexity with smaller models.
  • The paper also documents a "staircase learning" phenomenon, where scratchpads facilitate structured hierarchical learning, indicating reduced globality degrees and enhanced task complexity management.

Potential Future Directions and Implications

Visual scratchpads have promising implications for extending reasoning capabilities in vision-based AI systems. This research suggests avenues for further exploration:

  • Integration with Multi-modal Models: The adaptability of scratchpads can enhance vision-text models, merging step-by-step visual and textual reasoning, enabling tasks like visual geometry problem-solving and autonomous navigation.
  • Applications in Dynamic Content Generation: Understanding globality in visual contexts can refine image and video generation processes, ensuring continuity and cohesion amidst complex scenes.

The paper's foundational work on visual scratchpads proposes a critical advance in overcoming limitations of current vision models, advocating a structured approach to achieving sophisticated levels of global reasoning. As models evolve, the ability to engage deeply with visual context over multiple reasoning steps will become a valuable asset, creating robust systems adept at complex real-world interactions.

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