Breaking through the resolution limit: Byte and the University of Science and Technology of China reveal a large multi-modal document model

Now there are even large multi-modal high-resolution documents!

This technology can not only accurately identify the information in the image, but also call its own knowledge base to answer questions according to user needs

For example, when you see Mario’s interface in the picture, you can directly The answer is that this is a work of Nintendo.

This model was jointly researched by ByteDance and the University of Science and Technology of China, and was uploaded to arXiv on November 24, 2023

Study here , the author team proposed DocPedia, a unified high-resolution multi-modal document large model DocPedia.

In this study, the author used a new way to solve the shortcoming of existing models that cannot parse high-resolution document images.

DocPedia has a resolution of up to 2560×2560, but currently the industry’s advanced multi-modal large models such as LLaVA and MiniGPT-4 have an upper limit of image resolution of 336×336, which makes them unable to parse high-resolution document images.

So, how does this model perform and what kind of optimization method is used?

Significant improvement in various evaluation scores

In this paper, the author shows an example of DocPedia high-resolution image and text understanding. It can be observed that DocPedia has the ability to understand the content of instructions and accurately extract relevant graphic and text information from high-resolution document images and natural scene images

For example, in this set of pictures, DocPedia easily mines from the pictures With text information such as license plate number and computer configuration, even handwritten text can be accurately judged.

Combined with the text information in the image, DocPedia can also use large model reasoning capabilities to analyze problems based on context.

After reading the image information, DocPedia will also answer the extended content not shown in the image based on its rich world knowledge base

The following table quantitatively compares some existing multi-modal large models with DocPedia’s key information extraction (KIE) and visual question answering (VQA) capabilities.

By increasing the resolution and adopting effective training methods, we can see that DocPedia has achieved significant improvements on various test benchmarks

So, how does DocPedia achieve such an effect?

Solving the resolution problem from the frequency domain

DocPedia’s training is divided into two stages: pre-training and fine-tuning. In order to train DocPedia, the author team collected a large amount of graphic data containing various types of documents and built an instruction fine-tuning data set.

In the pre-training stage, the large language model will be frozen, and only the part of the visual encoder is optimized so that its output token representation space is consistent with the large language model

At this stage , the author team proposed to mainly train DocPedia’s perception capabilities, including the perception of text and natural scenes

The pre-training tasks include text detection, text recognition, end-to-end OCR, paragraph reading, full-text reading, and image text description .

In the fine-tuning phase, the large language model is unfrozen and end-to-end overall optimization is performed.

The author team proposed a perception-understanding joint training strategy: based on the original low-level perception tasks, add This joint perception-understanding training strategy further improves the performance of DocPedia.

In terms of strategy for resolution issues, unlike existing methods, DocPedia solves it from the

perspective. When processing high-resolution document images, DocPedia will first extract its DCT coefficient matrix. This matrix can downsample the spatial resolution by 8 times without losing the textual information of the original image.

After this step, we will use the cascaded frequency domain adapter (Frequency Adapter) Pass the input signal to the Vision Encoder for deeper resolution compression and feature extraction

With this method, a 2560×2560 image can be represented by 1600 tokens.

Compared with directly inputting the original image into a visual encoder (such as Swin Transformer), this method reduces the number of tokens by 4 times.

Finally, these tokens are spliced ​​with the tokens converted from the instructions in the sequence dimension and input into the large model for answer.

The results of the ablation experiment show that improving resolution and performing joint perception-understanding fine-tuning are two important factors to improve DocPedia performance

The following figure compares DocPedia's answer to a paper image and the same command under different input scales. It can be seen that DocPedia answers correctly if and only if the resolution is increased to 2560×2560.

The figure below compares DocPedia’s model responses to the same scene text image and the same instruction under different fine-tuning strategies.

It can be seen from this example that the model that has been jointly fine-tuned by perception and understanding can accurately perform text recognition and semantic question answering

##Please Click the following link to view the paper: https://arxiv.org/abs/2311.11810

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