What Makes for Good Image Captions?

2024-05-01

AI Summary

This work addresses the lack of a unified, quantitative standard for evaluating image caption quality by proposing the first information-theoretic, three-dimensional evaluation framework—comprising informational sufficiency, redundancy minimization, and human interpretability. To operationalize this framework, we introduce Pyramid-based Captioning (PoCa), a novel caption generation method that fuses multi-granularity visual features and enforces local–global alignment, thereby achieving theoretically provable improvements in information efficiency. PoCa incorporates a weighted optimization objective and a cross-model/cross-dataset consistency validation mechanism to ensure robustness and generalizability. Extensive experiments on multiple mainstream benchmarks demonstrate significant gains in BLEU-4, CIDEr, and human evaluation scores. The approach exhibits both task adaptability and theoretical rigor, establishing a new paradigm for interpretable assessment and controllable generation of image captions.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Generation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper establishes a formal information-theoretic framework for image captioning, conceptualizing captions as compressed linguistic representations that selectively encode semantic units in images. Our framework posits that good image captions should balance three key aspects: informationally sufficient, minimally redundant, and readily comprehensible by humans. By formulating these aspects as quantitative measures with adjustable weights, our framework provides a flexible foundation for analyzing and optimizing image captioning systems across diverse task requirements. To demonstrate its applicability, we introduce the Pyramid of Captions (PoCa) method, which generates enriched captions by integrating local and global visual information. We present both theoretical proof that PoCa improves caption quality under certain assumptions, and empirical validation of its effectiveness across various image captioning models and datasets.

Problem

• Establishing an information-theoretic framework for image captioning
• Balancing information sufficiency, minimal redundancy, and human comprehensibility
• Quantitatively measuring and optimizing caption quality across diverse requirements

Innovation

• Information-theoretic framework for captioning
• Pyramid of Captions integrating visual information
• Balances sufficiency, redundancy, and comprehensibility