High-Resolution Visual Document Assistant (HRVDA): Bridging the Gap Between Multimodal Language Models and Visual Document Understanding

핵심 개념

HRVDA bridges the gap between multimodal large language models (MLLMs) and visual document understanding by employing a content filtering mechanism and an instruction filtering module to efficiently process high-resolution document images.

초록

The paper proposes a novel multimodal large language model called HRVDA (High-Resolution Visual Document Assistant) to address the challenges in visual document understanding.
The key highlights are:

HRVDA employs a content filtering mechanism and an instruction filtering module to selectively filter out content-agnostic and instruction-agnostic visual tokens, making high-resolution image processing computationally feasible.

The content filtering mechanism utilizes a content detector to identify visual tokens containing valuable information, such as text, tables, and charts, and prunes the content-agnostic tokens. This significantly reduces the number of visual tokens, leading to faster training and inference.

The instruction filtering module further filters out instruction-agnostic visual tokens, focusing the model's attention on the regions relevant to the given instructions.

The authors construct a document-oriented visual instruction tuning dataset to enhance HRVDA's document modeling capabilities, covering a wide range of tasks including information extraction, text recognition, and visual question answering.

Extensive experiments demonstrate that HRVDA achieves state-of-the-art performance across multiple document understanding datasets, while maintaining training efficiency and inference speed comparable to low-resolution models.

통계

Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities.
However, their performance in visual document understanding still leaves much room for improvement, primarily due to the limitations posed by low-resolution image inputs and the lack of document-oriented visual instruction tuning.
HRVDA employs a content filtering mechanism that can conservatively estimate to filter out approximately 50% of content-agnostic tokens, resulting in a substantial reduction of 30% in training and inference latency without compromising performance.

인용구

"Directly increasing the image resolution generates a large number of visual tokens, which will occupy the limited input capacity of LLMs, and induce considerable training costs and inference latency."
"Unlike ordinary images, document images possess distinct layout and structural information, where the font, style, and color hold significant importance for comprehending the content."

핵심 통찰 요약

HRVDA

by Chaohu Liu,K... 게시일 arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06918.pdf

더 깊은 질문

How can HRVDA's content filtering and instruction filtering mechanisms be further improved to achieve even higher computational efficiency without sacrificing performance?

To enhance HRVDA's content filtering and instruction filtering mechanisms for improved computational efficiency without compromising performance, several strategies can be implemented:

Dynamic Threshold Adjustment: Implementing dynamic threshold adjustment based on the complexity of the input images can help optimize the filtering process. By dynamically adjusting the filtering thresholds according to the content density and instruction specificity of the images, HRVDA can efficiently filter out irrelevant visual tokens while retaining essential information.

Selective Token Pruning: Introducing a selective token pruning mechanism that prioritizes the removal of redundant tokens based on their relevance to the task at hand can further streamline the filtering process. By focusing on pruning tokens that contribute minimally to the final prediction, HRVDA can reduce computational overhead without compromising performance.

Hierarchical Filtering: Implementing a hierarchical filtering approach where visual tokens are filtered at multiple stages based on their importance can improve computational efficiency. By initially applying coarse filtering to remove obvious irrelevant tokens followed by more refined filtering mechanisms, HRVDA can achieve higher efficiency in processing high-resolution images.

Adaptive Filtering Strategies: Incorporating adaptive filtering strategies that dynamically adjust the filtering mechanisms during inference based on real-time feedback can optimize computational efficiency. By continuously evaluating the impact of filtering on performance and adjusting the filtering parameters accordingly, HRVDA can adapt to different input scenarios effectively.

What are the potential limitations or drawbacks of the document-oriented visual instruction tuning dataset, and how can it be expanded or diversified to better capture the complexity of real-world document understanding tasks?

The document-oriented visual instruction tuning dataset may have the following limitations:

Limited Task Coverage: The dataset may not cover a comprehensive range of document understanding tasks, limiting the model's ability to generalize across diverse scenarios.

Lack of Variability: The dataset may lack variability in terms of document layouts, styles, and content types, which can hinder the model's adaptability to real-world document variations.

Data Bias: The dataset may exhibit biases towards specific types of documents or instructions, leading to skewed model performance on certain tasks.

To address these limitations and enhance the dataset for better capturing the complexity of real-world document understanding tasks, the following strategies can be implemented:

Task Expansion: Include a broader range of document understanding tasks such as document summarization, document classification, and document retrieval to diversify the dataset and improve the model's versatility.

Data Augmentation: Introduce data augmentation techniques to increase the variability in document layouts, styles, and content, ensuring the model is exposed to a wide range of document types.

Crowdsourced Annotations: Engage human annotators to provide diverse and nuanced instructions for a more comprehensive dataset that reflects real-world document understanding challenges.

Domain-Specific Data: Incorporate domain-specific data from various industries such as healthcare, finance, and legal sectors to ensure the dataset captures the intricacies of domain-specific document understanding tasks.

Given the advancements in HRVDA, how can the model's capabilities be extended beyond document understanding to tackle other challenging multimodal tasks, such as visual reasoning or multimodal dialogue systems?

To extend HRVDA's capabilities beyond document understanding and tackle challenging multimodal tasks like visual reasoning and multimodal dialogue systems, the following approaches can be considered:

Task-Specific Fine-Tuning: Fine-tune HRVDA on datasets specific to visual reasoning tasks such as visual question answering or image captioning to adapt the model to diverse multimodal challenges.

Cross-Modal Knowledge Transfer: Implement cross-modal knowledge transfer techniques to leverage the model's document understanding capabilities for tasks like visual reasoning. By transferring knowledge learned from document understanding to new tasks, HRVDA can generalize better across multimodal domains.

Incremental Learning: Implement incremental learning strategies to gradually introduce new tasks and modalities to HRVDA, allowing the model to adapt and learn progressively complex multimodal tasks over time.

Interactive Learning: Incorporate interactive learning paradigms where HRVDA engages in dialogues with users to improve its multimodal reasoning and dialogue capabilities. By interacting with users in a conversational manner, the model can enhance its understanding of context and improve its dialogue responses.

By incorporating these strategies, HRVDA can expand its capabilities beyond document understanding and become a versatile model for a wide range of challenging multimodal tasks.