A Novel Evaluation Framework for Image2Text Generation

• URL: http://arxiv.org/abs/2408.01723v1
• Date: Sat, 3 Aug 2024 09:27:57 GMT
• Title: A Novel Evaluation Framework for Image2Text Generation
• Authors: Jia-Hong Huang, Hongyi Zhu, Yixian Shen, Stevan Rudinac, Alessio M. Pacces, Evangelos Kanoulas,
• Abstract summary: We propose an evaluation framework rooted in a modern large language model (LLM) capable of image generation. A high similarity score suggests that the image captioning model has accurately generated textual descriptions. A low similarity score indicates discrepancies, revealing potential shortcomings in the model's performance.
• Score: 15.10524860121122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the quality of automatically generated image descriptions is challenging, requiring metrics that capture various aspects such as grammaticality, coverage, correctness, and truthfulness. While human evaluation offers valuable insights, its cost and time-consuming nature pose limitations. Existing automated metrics like BLEU, ROUGE, METEOR, and CIDEr aim to bridge this gap but often show weak correlations with human judgment. We address this challenge by introducing a novel evaluation framework rooted in a modern large language model (LLM), such as GPT-4 or Gemini, capable of image generation. In our proposed framework, we begin by feeding an input image into a designated image captioning model, chosen for evaluation, to generate a textual description. Using this description, an LLM then creates a new image. By extracting features from both the original and LLM-created images, we measure their similarity using a designated similarity metric. A high similarity score suggests that the image captioning model has accurately generated textual descriptions, while a low similarity score indicates discrepancies, revealing potential shortcomings in the model's performance. Human-annotated reference captions are not required in our proposed evaluation framework, which serves as a valuable tool for evaluating the effectiveness of image captioning models. Its efficacy is confirmed through human evaluation.

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