Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models

TL;DR

This paper introduces AETNet, a fine-tuning approach that incorporates alignment-aware contrastive objectives and additional transformers to improve patch-level document image understanding, achieving state-of-the-art results.

Contribution

Proposes a novel alignment-enriched tuning framework with extra transformers and contrastive objectives for better downstream task adaptation of pre-trained document image models.

Findings

AETNet outperforms existing models like LayoutLMv3 on multiple tasks.

Incorporating alignment objectives improves patch-level and structural understanding.

Fine-tuning with alignment-aware contrastive learning enhances downstream performance.

Abstract

Alignment between image and text has shown promising improvements on patch-level pre-trained document image models. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question naturally arises: Could we fine-tune the pre-trained models adaptive to downstream tasks with alignment objectives and achieve comparable or better performance? In this paper, we propose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific supervised and alignment-aware contrastive objective. Specifically, we introduce an extra visual transformer as the alignment-ware image encoder and an extra text transformer as the alignment-ware text encoder before multimodal fusion. We consider alignment in the following three aspects: 1) document-level alignment by leveraging the cross-modal and intra-modal contrastive loss; 2) global-local alignment for modeling localized and structural information in document images; and 3) local-level alignment for more accurate patch-level information. Experiments on various downstream tasks show that AETNet can achieve state-of-the-art performance on various downstream tasks. Notably, AETNet consistently outperforms state-of-the-art pre-trained models, such as LayoutLMv3 with fine-tuning techniques, on three different downstream tasks.