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| Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
| Title |
Document Collection Visual Question Answering |
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Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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| Volume |
12822 |
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Pages |
778-792 |
| Keywords |
Document collection; Visual Question Answering |
| Abstract |
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task. |
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ICDAR |
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DAG; 600.121 |
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no |
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Admin @ si @ TKV2021 |
Serial |
3622 |
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| Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
| Title |
Hierarchical multimodal transformers for Multi-Page DocVQA |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
144 |
Issue |
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Pages |
109834 |
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| Abstract |
Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure. |
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ISSN 0031-3203 |
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DAG; 600.155; 600.121 |
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no |
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Admin @ si @ TKV2023 |
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3825 |
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| Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
| Title |
Hierarchical multimodal transformers for Multipage DocVQA |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
144 |
Issue |
109834 |
Pages |
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| Abstract |
Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure. |
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DAG |
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Admin @ si @ TKV2023 |
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3836 |
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