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Prompting Large Vision-Language Models for Compositional Reasoning (2401.11337v1)

Published 20 Jan 2024 in cs.CV and cs.AI
Prompting Large Vision-Language Models for Compositional Reasoning

Abstract: Vision-LLMs such as CLIP have shown impressive capabilities in encoding texts and images into aligned embeddings, enabling the retrieval of multimodal data in a shared embedding space. However, these embedding-based models still face challenges in effectively matching images and texts with similar visio-linguistic compositionality, as evidenced by their performance on the recent Winoground dataset. In this paper, we argue that this limitation stems from two factors: the use of single vector representations for complex multimodal data, and the absence of step-by-step reasoning in these embedding-based methods. To address this issue, we make an exploratory step using a novel generative method that prompts large vision-LLMs (e.g., GPT-4) to depict images and perform compositional reasoning. Our method outperforms other embedding-based methods on the Winoground dataset, and obtains further improvement of up to 10% accuracy when enhanced with the optimal description.

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Analyzing Prompting Strategies for Compositional Reasoning in Vision-LLMs

The paper "Prompting Large Vision-LLMs for Compositional Reasoning" presents a novel exploration into the limitations and capabilities of Vision-LLMs (VLMs) with respect to compositional reasoning. Specifically, the research addresses the challenges faced by embedding-based approaches in tasks requiring nuanced understanding of visual and textual data compositionality, with a focus on the Winoground dataset. The central contribution of this paper is the development of a generative approach, termed KeyComp, that exploits the potential of large vision-LLMs, like GPT-4, to overcome these challenges.

Technical Overview

KeyComp addresses two primary limitations identified in existing embedding-based models: the reliance on single vector representations for complex multimodal data and the absence of step-by-step reasoning processes. These limitations hinder the models' abilities to discern intricate relationships between objects in visual data and their textual descriptions. To mitigate this, KeyComp introduces a multi-step generative method that enhances model performance in compositional reasoning.

KeyComp's approach comprises three core stages:

  1. Keyword Detection: Keywords are extracted from the caption text to focus the vision model's attention on relevant image details, guiding the visual representation process.
  2. Keyword-guided Image Description: A VLM generates detailed descriptions of image content guided by the previously identified keywords, enabling the representation of key entities and their relations in the images.
  3. Reasoning with LLMs: The descriptions are analyzed with an LLM to perform stepwise reasoning, yielding improved selection accuracy for image-to-text and text-to-image matching tasks.

The proposed methodology leverages the advanced reasoning capabilities inherent in LLMs over weaker VLM counterparts, providing substantial gains in performance metrics when benchmarked against state-of-the-art embedding-based methods.

Empirical Results

KeyComp achieves significant improvements in image scoring on the Winoground dataset, outperforming established models like CLIP, IAIS, and CACR by notable margins. With a clear enhancement of 5.1% in image scoring accuracy, the paper highlights the effectiveness of its generative approach in handling complex examples and non-standard images. Furthermore, error analysis reveals gaps in VLMs' current spatial reasoning capabilities and illuminates potential areas of refinement, such as improving image content descriptions and better interpreting syntactic complexity.

Implications and Future Directions

The findings underscore the importance of fine-grained reasoning in VLMs, suggesting that leveraging keyword guidance and multi-step reasoning substantially elevates the quality of image descriptions and matching accuracy. From a theoretical standpoint, this work advances our understanding of multimodal representations and the mechanisms necessary for compositional reasoning.

Looking forward, enhancing the spatial reasoning abilities of VLMs emerges as a key area for future research. The development of effective prompting strategies, capable of directing models to crucial image areas suitably, and advances in handling spatial and partial object reasoning may significantly improve VLM performance. Additionally, research could explore further integration of LLMs with refined visual inputs to enrich reasoning outputs more reliably.

In conclusion, this paper contributes constructively to the field by providing a methodological framework and experimental insights into leveraging generative techniques for advancing compositional reasoning in VLMs. The strategies introduced offer a promising pathway for creating more robust and intelligent vision-language systems that can handle a broader range of tasks with higher precision.

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Authors (3)
  1. Timothy Ossowski (7 papers)
  2. Ming Jiang (59 papers)
  3. Junjie Hu (111 papers)
Citations (2)
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