FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction

• URL: http://arxiv.org/abs/2305.02549v2
• Date: Tue, 13 Jun 2023 04:27:14 GMT
• Title: FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
• Authors: Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolai Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister
• Abstract summary: We introduce a centralized graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.
• Score: 43.17713130538514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. The graph contrastive objective maximizes the agreement of multimodal representations, providing a natural interplay for all modalities without special customization. In addition, we extract image features within the bounding box that joins a pair of tokens connected by a graph edge, capturing more targeted visual cues without loading a sophisticated and separately pre-trained image embedder. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.

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