Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models

Core Concepts

DoCo enhances LVLMs by addressing fine-grained feature collapse, aligning visual and multimodal features for text-rich scenarios.

Abstract

The paper introduces DoCo, a contrastive learning framework to improve LVLMs' performance in text-rich scenarios. By aligning visual and multimodal features at the document object level, DoCo addresses the fine-grained feature collapse issue. Experimental results demonstrate superior performance on various VDU benchmarks.
The importance of fine-grained features in VDU tasks is highlighted, with DoCo serving as a plug-and-play pre-training method. The integration of Intra-DoCo and Inter-DoCo significantly enhances LVLMs' comprehension of text-rich documents. The ROI Aggregation module improves feature extraction by focusing on regions of interest.
Extensive ablation studies confirm the effectiveness of Intra-DoCo, Inter-DoCo, ROI Aggregation, and alignment across different modalities in enhancing LVLM performance. Qualitative results show that DoCo assists the vision encoder in capturing more effective visual cues for better understanding of text-rich images.
Further analysis showcases how DoCo guides LVLMs to capture salient details comprehensively, crucial for tasks involving visual document understanding. Visualization heat-maps illustrate the contrast between CLIP and DoCo models in capturing fine-grained textual features for improved comprehension.

Stats

1 epoch: 1 million image-text pairs pre-training dataset.
Batch size: 640 during pre-training, 256 during fine-tuning.
Maximum learning rate: 2e−4, minimum learning rate: 1e−6.
Weight decay: 5e−2, gradient clipping: 1.0.

Quotes

"DoCo leverages an auxiliary multimodal encoder to obtain the features of document objects and align them to the visual features generated by the vision encoder."
"We investigate the importance of fine-grained features in VDU tasks and propose the fine-grained feature collapse issue."

Key Insights Distilled From

Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models

by Xin Li,Yunfe... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19014.pdf

Deeper Inquiries

How does DoCo's approach compare to traditional image-level contrastive learning methods?

DoCo's approach differs from traditional image-level contrastive learning methods in several key aspects. Traditional methods focus on discriminating the entire image instance between visual and textual inputs at a holistic level, aiming to learn general representations but often failing to extract fine-grained features in text-rich scenarios. On the other hand, DoCo discriminates document objects within an image and across images, aligning multimodal features with visual representations at a more localized level. This allows for the extraction of fine-grained features specific to document elements, enhancing the model's ability to understand text-rich documents comprehensively.

What are potential implications of integrating Intra-DoCo and Inter-DoCo into other LVLM frameworks?

Integrating Intra-DoCo and Inter-DoCo into other LVLM frameworks could have significant implications for improving their performance in visual document understanding tasks.

Enhanced Fine-Grained Features: The integration of Intra-DoCo can help models capture detailed information at the object level within images, leading to better representation learning for individual document elements.

Global Context Understanding: By incorporating Inter-DoCo, models can gain a broader perspective by considering interactions between different images during training. This can improve contextual understanding across diverse documents.

Improved Generalization: The combination of both approaches can enhance the model's ability to generalize well on various VDU tasks by leveraging local details as well as global context information effectively.

Better Cross-Domain Performance: Integrating these components may enable LVLMs to perform more robustly across different types of visual documents by honing their feature extraction capabilities at multiple levels simultaneously.

How might DoCo's methodology impact future developments in visual document understanding research?

The methodology employed by DoCO has several implications for future developments in visual document understanding research:

Fine-Grained Feature Extraction: By focusing on extracting fine-grained features from text-rich scenarios, DoCO sets a precedent for future research emphasizing detailed analysis within complex documents.

Contrastive Learning Paradigm: The success of using contrastive learning techniques tailored specifically for VDU tasks opens up avenues for exploring similar methodologies in other multimodal applications where precise feature alignment is crucial.

Plug-and-play Pre-training Methods: The plug-and-play nature of DoCO as a pre-training method that seamlessly integrates with existing LVLMs without increasing computational complexity offers scalability and applicability across various models and datasets.

4Advancements in Multimodal Understanding: Future research may build upon DoCO’s framework to further enhance multimodal understanding capabilities beyond VDU tasks, potentially leading to breakthroughs in areas like medical imaging analysis or autonomous systems requiring rich sensory data interpretation.