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Analyzing the Roles of Language and Vision in Learning from Limited Data

Allison Chen, Ilia Sucholutsky, Olga Russakovsky, Thomas L. Griffiths

TL;DR

This work interrogates how language and vision contribute to visual understanding under limited data by ablating components in Vision-Language Models. Using a cognitive-architecture framework, the authors dissect visual processing, prior knowledge, and reasoning, evaluating their contributions with three simulations on a text-rich ImageNet-derived dataset. They find that a language-only system with prior knowledge, reasoning, and a few examples can achieve roughly $75\%$ of a full VLM's performance, and that removing any single language component markedly degrades performance, while vision-alone models lag behind. The results imply that language supplies critical prior knowledge and reasoning that substantially support visual understanding, offering a path toward more transparent analysis of VLM/LLM systems and highlighting the value of language-for-vision in learning from limited data.

Abstract

Does language help make sense of the visual world? How important is it to actually see the world rather than having it described with words? These basic questions about the nature of intelligence have been difficult to answer because we only had one example of an intelligent system -- humans -- and limited access to cases that isolated language or vision. However, the development of sophisticated Vision-Language Models (VLMs) by artificial intelligence researchers offers us new opportunities to explore the contributions that language and vision make to learning about the world. We ablate components from the cognitive architecture of these models to identify their contributions to learning new tasks from limited data. We find that a language model leveraging all components recovers a majority of a VLM's performance, despite its lack of visual input, and that language seems to allow this by providing access to prior knowledge and reasoning.

Analyzing the Roles of Language and Vision in Learning from Limited Data

TL;DR

This work interrogates how language and vision contribute to visual understanding under limited data by ablating components in Vision-Language Models. Using a cognitive-architecture framework, the authors dissect visual processing, prior knowledge, and reasoning, evaluating their contributions with three simulations on a text-rich ImageNet-derived dataset. They find that a language-only system with prior knowledge, reasoning, and a few examples can achieve roughly of a full VLM's performance, and that removing any single language component markedly degrades performance, while vision-alone models lag behind. The results imply that language supplies critical prior knowledge and reasoning that substantially support visual understanding, offering a path toward more transparent analysis of VLM/LLM systems and highlighting the value of language-for-vision in learning from limited data.

Abstract

Does language help make sense of the visual world? How important is it to actually see the world rather than having it described with words? These basic questions about the nature of intelligence have been difficult to answer because we only had one example of an intelligent system -- humans -- and limited access to cases that isolated language or vision. However, the development of sophisticated Vision-Language Models (VLMs) by artificial intelligence researchers offers us new opportunities to explore the contributions that language and vision make to learning about the world. We ablate components from the cognitive architecture of these models to identify their contributions to learning new tasks from limited data. We find that a language model leveraging all components recovers a majority of a VLM's performance, despite its lack of visual input, and that language seems to allow this by providing access to prior knowledge and reasoning.
Paper Structure (14 sections, 4 figures)

This paper contains 14 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Large Language Models & Vision-Language Models
  4. Language and Visual Understanding
  5. Exploring the Space of Cognitive Architectures
  6. Methods
  7. Data Curation
  8. Implementations of Cognitive Architectures
  9. Simulations
  10. Results
  11. Discussion
  12. Limitations and Future Work
  13. Conclusion
  14. Acknowledgements.

Figures (4)

  • Figure 1: Hypothesized cognitive architecture for intelligent visual recognition systems. A full architecture consists of visual processing, prior knowledge, and reasoning components and is given relevant training examples prior to testing.
  • Figure 2: Examples of images, corresponding tags, captions, and the class labels. [...] indicates the text was truncated due to length. First row represents a clean example and the bottom three demonstrate variance in the data.
  • Figure 3: Cognitive architectures used in simulations. Top Left: A full vision language model but lacking examples. Top Right: Possible architectures when we remove language. Bottom: Possible architectures when removing vision. The bottom left box shows a full LLM, and the right three represent removing one component at a time: examples, knowledge, or reasoning. Colored boxes are used to differentiate these models in subsequent presentation of the results.
  • Figure 4: Results from simulations. The horizontal axis lists components present in each setting where bold indicates component present and gray and italicized indicates component missing. Vision, knowledge, and reasoning are part of a model's architecture and examples refers to the model seeing relevant examples of the new task. The highest bar in dark blue titled Vision-Knowledge-Reasoning is the full VLM. To the right we isolated language and its various components, and to the left, we isolated components of vision. Bar colors correspond to the boxes around models shown in Fig. \ref{['fig:cog_arch_all']}. Error bars represent standard error of the mean calculated by treating each trial as a binomial random variable.