Generative Compositor for Few-Shot Visual Information Extraction

• URL: http://arxiv.org/abs/2503.16854v1
• Date: Fri, 21 Mar 2025 04:56:24 GMT
• Title: Generative Compositor for Few-Shot Visual Information Extraction
• Authors: Zhibo Yang, Wei Hua, Sibo Song, Cong Yao, Yingying Zhu, Wenqing Cheng, Xiang Bai,
• Abstract summary: We propose a novel generative model, named Generative generative spatialtor, to address the challenge of few-shot VIE.<n>Generative generative spatialtor is a hybrid pointer-generator network that emulates the operations of a compositor by retrieving words from the source text.<n>The proposed method achieves highly competitive results in the full-sample training, while notably outperforms the baseline in the 1-shot, 5-shot, and 10-shot settings.
• Score: 60.663887314625164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Information Extraction (VIE), aiming at extracting structured information from visually rich document images, plays a pivotal role in document processing. Considering various layouts, semantic scopes, and languages, VIE encompasses an extensive range of types, potentially numbering in the thousands. However, many of these types suffer from a lack of training data, which poses significant challenges. In this paper, we propose a novel generative model, named Generative Compositor, to address the challenge of few-shot VIE. The Generative Compositor is a hybrid pointer-generator network that emulates the operations of a compositor by retrieving words from the source text and assembling them based on the provided prompts. Furthermore, three pre-training strategies are employed to enhance the model's perception of spatial context information. Besides, a prompt-aware resampler is specially designed to enable efficient matching by leveraging the entity-semantic prior contained in prompts. The introduction of the prompt-based retrieval mechanism and the pre-training strategies enable the model to acquire more effective spatial and semantic clues with limited training samples. Experiments demonstrate that the proposed method achieves highly competitive results in the full-sample training, while notably outperforms the baseline in the 1-shot, 5-shot, and 10-shot settings.

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