Understanding multi-page paperwork and information movies is a typical job in human each day life. To sort out such situations, Multimodal Giant Language Fashions (MLLMs) ought to be outfitted with the power to know a number of photographs with wealthy visually-situated textual content data. Nonetheless, comprehending doc photographs is more difficult than pure photographs, because it requires a extra fine-grained notion to acknowledge all texts. Current approaches both add a high-resolution encoder or crop high-resolution photographs into low-resolution sub-images, each of which have limitations.
Earlier researchers have tried to unravel the problem of understanding doc photographs utilizing varied strategies. Some works proposed including a high-resolution encoder to higher seize the fine-grained textual content data in doc photographs. Others selected to crop high-resolution photographs into low-resolution sub-images and let the Giant Language Mannequin perceive their relationship.
Whereas these approaches have achieved promising efficiency, they undergo from a typical challenge – the massive variety of visible tokens required to signify a single doc picture. For instance, the InternVL 2 mannequin prices a median of 3k visible tokens on the single-page doc understanding benchmark DocVQA. Such lengthy visible token sequences not solely lead to lengthy inference instances but in addition occupy a major quantity of GPU reminiscence, vastly limiting their software in situations that contain understanding full paperwork or movies.
Researchers from Alibaba Group and Renmin College of China have proposed a sturdy compressing structure referred to as Excessive-resolution DocCompressor. This methodology makes use of the visible options of a worldwide low-resolution picture because the compressing steerage (question), as the worldwide function map can successfully seize the general format data of the doc.
As an alternative of attending to all high-resolution options, the Excessive-resolution DocCompressor collects a bunch of high-resolution options with similar relative positions within the uncooked picture because the compressing objects for every question from the worldwide function map. This layout-aware method helps to higher summarize the textual content data inside a selected format area.
As well as, the researchers argue that compressing visible options after the vision-to-text module of the Multimodal Giant Language Mannequin can higher keep the textual semantics within the doc photographs, as that is analogous to summarizing texts in Pure Language Processing.
The DocOwl2 mannequin makes use of a Form-adaptive Cropping Module and a low-resolution imaginative and prescient encoder to encode high-resolution doc photographs. The Form-adaptive Cropping Module cuts the uncooked picture into a number of low-resolution sub-images, and the low-resolution imaginative and prescient encoder is used to encode each the sub-images and the worldwide picture. The mannequin then makes use of a vision-to-text module referred to as H-Reducer to ensemble the horizontal visible options and align the dimension of the imaginative and prescient options with the Giant Language Mannequin. Along with that, DocOwl2 features a high-resolution compressor, which is the important thing part of the Excessive-resolution DocCompressor. This compressor makes use of the visible options of the worldwide low-resolution picture because the question and collects a bunch of high-resolution options with similar relative positions within the uncooked picture because the compressing objects for every question. This layout-aware method helps to higher summarize the textual content data inside a selected format area. Lastly, the compressed visible tokens of a number of photographs or pages are concatenated with textual content directions and enter to a Giant Language Mannequin for multimodal understanding.
The researchers in contrast the DocOwl2 mannequin with state-of-the-art Multimodal Giant Language Fashions on 10 single-image doc understanding benchmarks, 2 multi-page doc understanding benchmarks, and 1 text-rich video understanding benchmark. They thought of each the question-answering efficiency (measured by ANLS) and the First Token Latency (in seconds) to guage the effectiveness of their mannequin. For the single-image doc understanding job, the researchers divided the baselines into three teams: (a) fashions with out Giant Language Fashions as decoders, (b) Multimodal LLMs with a median of over 1,000 visible tokens per doc picture, and (c) Multimodal LLMs with lower than 1,000 visible tokens.
The outcomes present that though the fashions particularly fine-tuned on every downstream dataset carried out effectively, the Multimodal LLMs demonstrated the potential for generalized OCR-free doc understanding. In comparison with different Multimodal LLMs with lower than 1,000 visible tokens, the DocOwl2 mannequin achieved higher or comparable efficiency on the ten benchmarks. Notably, with fewer visible tokens, DocOwl2 outperformed fashions like TextMonkey and TokenPacker, which additionally aimed to compress visible tokens, demonstrating the effectiveness of the Excessive-resolution DocCompressor.
Additionally, when in comparison with state-of-the-art Multimodal LLMs with over 1,000 visible tokens, the DocOwl2 mannequin achieved over 80% of their efficiency whereas utilizing lower than 20% of the visible tokens. For the multi-page doc understanding and text-rich video understanding duties, the DocOwl2 mannequin additionally demonstrated superior efficiency and considerably decrease First Token Latency in comparison with different Multimodal LLMs that may be fed greater than 10 photographs below a single A100-80G GPU.
This examine presents mPLUG-DocOwl2, a Multimodal Giant Language Mannequin able to environment friendly OCR-free Multi-page Doc Understanding. The strong Excessive-resolution DocCompressor structure compresses every high-resolution doc picture into simply 324 tokens utilizing cross-attention with international visible options as steerage. On single-image benchmarks, DocOwl2 outperforms current compressing strategies and matches state-of-the-art MLLMs whereas utilizing fewer visible tokens. It additionally achieves OCR-free state-of-the-art efficiency on multi-page doc and text-rich video understanding duties with a lot decrease latency. The researchers emphasize that utilizing hundreds of visible tokens per doc web page is usually redundant and a waste of computational assets. They hope that DocOwl2 will carry consideration to balancing environment friendly picture illustration and high-performance doc understanding.
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