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Author |
Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes |
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Title |
On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition |
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Conference Article |
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Year |
2021 |
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International Society for Music Information Retrieval Conference |
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690-696 |
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Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts. |
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Virtual; November 2021 |
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ISMIR |
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DAG; 600.140; 600.121 |
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Admin @ si @ TBK2021 |
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3616 |
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Author |
Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi |
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Title |
Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images |
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Conference Article |
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2021 |
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4th International Conference on Historical Cryptology |
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34-37 |
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Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering. |
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Virtual; September 2021 |
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HistoCrypt |
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DAG; 602.230; 600.140; 600.121 |
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Admin @ si @ CSF2021 |
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3617 |
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Author |
Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
A Transcription Is All You Need: Learning to Align through Attention |
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Conference Article |
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2021 |
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14th IAPR International Workshop on Graphics Recognition |
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12916 |
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141–146 |
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Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. |
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Virtual; September 2021 |
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GREC |
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DAG; 602.230; 600.140; 600.121 |
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Admin @ si @ TSC2021 |
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3619 |
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Author |
Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Multimodal grid features and cell pointers for scene text visual question answering |
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2021 |
Publication |
Pattern Recognition Letters |
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PRL |
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150 |
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242-249 |
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This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link. |
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DAG; 600.084; 600.121 |
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Admin @ si @ GBT2021 |
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3620 |
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Author |
Minesh Mathew; Lluis Gomez; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
Asking questions on handwritten document collections |
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Journal Article |
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Year |
2021 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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24 |
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235-249 |
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This work addresses the problem of Question Answering (QA) on handwritten document collections. Unlike typical QA and Visual Question Answering (VQA) formulations where the answer is a short text, we aim to locate a document snippet where the answer lies. The proposed approach works without recognizing the text in the documents. We argue that the recognition-free approach is suitable for handwritten documents and historical collections where robust text recognition is often difficult. At the same time, for human users, document image snippets containing answers act as a valid alternative to textual answers. The proposed approach uses an off-the-shelf deep embedding network which can project both textual words and word images into a common sub-space. This embedding bridges the textual and visual domains and helps us retrieve document snippets that potentially answer a question. We evaluate results of the proposed approach on two new datasets: (i) HW-SQuAD: a synthetic, handwritten document image counterpart of SQuAD1.0 dataset and (ii) BenthamQA: a smaller set of QA pairs defined on documents from the popular Bentham manuscripts collection. We also present a thorough analysis of the proposed recognition-free approach compared to a recognition-based approach which uses text recognized from the images using an OCR. Datasets presented in this work are available to download at docvqa.org. |
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DAG; 600.121 |
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Admin @ si @ MGK2021 |
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3621 |
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Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
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Title |
Document Collection Visual Question Answering |
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Conference Article |
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Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12822 |
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778-792 |
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Document collection; Visual Question Answering |
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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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Admin @ si @ TKV2021 |
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3622 |
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Author |
Ruben Tito; Minesh Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
ICDAR 2021 Competition on Document Visual Question Answering |
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Conference Article |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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635-649 |
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In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented. |
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VIRTUAL; Lausanne; Suissa; September 2021 |
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ICDAR |
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DAG; 600.121 |
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Admin @ si @ TMJ2021 |
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3624 |
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Minesh Mathew; Viraj Bagal; Ruben Tito; Dimosthenis Karatzas; Ernest Valveny; C.V. Jawahar |
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InfographicVQA |
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2022 |
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Winter Conference on Applications of Computer Vision |
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1697-1706 |
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Document Analysis Datasets; Evaluation and Comparison of Vision Algorithms; Vision and Languages |
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Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics--images that are designed to communicate information quickly and clearly to human brain--is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa. org |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
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DAG; 600.155 |
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MBT2022 |
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3625 |
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Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning |
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2022 |
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Winter Conference on Applications of Computer Vision |
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1381-1390 |
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Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data |
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Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase
in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online. |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
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DAG; 600.155; 302.105 |
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Admin @ si @ BGK2022 |
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3662 |
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Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching |
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2022 |
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Winter Conference on Applications of Computer Vision |
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1391-1400 |
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Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning |
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The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin. |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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DAG; 600.155; 302.105; |
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Admin @ si @ BMG2022 |
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3663 |
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