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Author | Patricia Suarez; Dario Carpio; Angel Sappa | ||||
Title | Boosting Guided Super-Resolution Performance with Synthesized Images | Type | Conference Article | ||
Year | 2023 | Publication | 17th International Conference on Signal-Image Technology & Internet-Based Systems | Abbreviated Journal | |
Volume | Issue | Pages | 189-195 | ||
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Abstract | Guided image processing techniques are widely used for extracting information from a guiding image to aid in the processing of the guided one. These images may be sourced from different modalities, such as 2D and 3D, or different spectral bands, like visible and infrared. In the case of guided cross-spectral super-resolution, features from the two modal images are extracted and efficiently merged to migrate guidance information from one image, usually high-resolution (HR), toward the guided one, usually low-resolution (LR). Different approaches have been recently proposed focusing on the development of architectures for feature extraction and merging in the cross-spectral domains, but none of them care about the different nature of the given images. This paper focuses on the specific problem of guided thermal image super-resolution, where an LR thermal image is enhanced by an HR visible spectrum image. To improve existing guided super-resolution techniques, a novel scheme is proposed that maps the original guiding information to a thermal image-like representation that is similar to the output. Experimental results evaluating five different approaches demonstrate that the best results are achieved when the guiding and guided images share the same domain. | ||||
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Area | Expedition | Conference | SITIS | ||
Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ SCS2023c | Serial | 4011 | ||
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Author | Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes | ||||
Title | Lost in Transcription of Graphic Signs in Ciphers | Type | Conference Article | ||
Year | 2022 | Publication | International Conference on Historical Cryptology (HistoCrypt 2022) | Abbreviated Journal | |
Volume | Issue | Pages | 153-158 | ||
Keywords | transcription of ciphers; hand-written text recognition of symbols; graphic signs | ||||
Abstract | Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. | ||||
Address | Amsterdam, Netherlands, June 20-22, 2022 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | HystoCrypt | ||
Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
Call Number | Admin @ si @ MBS2022 | Serial | 3731 | ||
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Author | Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados | ||||
Title | One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition | Type | Conference Article | ||
Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Document Analysis | ||||
Abstract | Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. | ||||
Address | Virtual; January 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | DAG; 602.230; 600.140 | Approved | no | ||
Call Number | Admin @ si @ SBD2022 | Serial | 3615 | ||
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Author | Minesh Mathew; Viraj Bagal; Ruben Tito; Dimosthenis Karatzas; Ernest Valveny; C.V. Jawahar | ||||
Title | InfographicVQA | Type | Conference Article | ||
Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1697-1706 | ||
Keywords | Document Analysis Datasets; Evaluation and Comparison of Vision Algorithms; Vision and Languages | ||||
Abstract | 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 | ||||
Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | DAG; 600.155 | Approved | no | ||
Call Number | MBT2022 | Serial | 3625 | ||
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Author | Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund | ||||
Title | Multi-Task Classification of Sewer Pipe Defects and Properties Using a Cross-Task Graph Neural Network Decoder | Type | Conference Article | ||
Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2806-2817 | ||
Keywords | Vision Systems; Applications Multi-Task Classification | ||||
Abstract | The sewerage infrastructure is one of the most important and expensive infrastructures in modern society. In order to efficiently manage the sewerage infrastructure, automated sewer inspection has to be utilized. However, while sewer
defect classification has been investigated for decades, little attention has been given to classifying sewer pipe properties such as water level, pipe material, and pipe shape, which are needed to evaluate the level of sewer pipe deterioration. In this work we classify sewer pipe defects and properties concurrently and present a novel decoder-focused multi-task classification architecture Cross-Task Graph Neural Network (CT-GNN), which refines the disjointed per-task predictions using cross-task information. The CT-GNN architecture extends the traditional disjointed task-heads decoder, by utilizing a cross-task graph and unique class node embeddings. The cross-task graph can either be determined a priori based on the conditional probability between the task classes or determined dynamically using self-attention. CT-GNN can be added to any backbone and trained end-toend at a small increase in the parameter count. We achieve state-of-the-art performance on all four classification tasks in the Sewer-ML dataset, improving defect classification and water level classification by 5.3 and 8.0 percentage points, respectively. We also outperform the single task methods as well as other multi-task classification approaches while introducing 50 times fewer parameters than previous modelfocused approaches. |
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ BME2022 | Serial | 3638 | ||
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Author | Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning | Type | Conference Article | ||
Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1381-1390 | ||
Keywords | Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data | ||||
Abstract | 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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Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | DAG; 600.155; 302.105 | Approved | no | ||
Call Number | Admin @ si @ BGK2022 | Serial | 3662 | ||
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Author | Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching | Type | Conference Article | ||
Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1391-1400 | ||
Keywords | Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning | ||||
Abstract | 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. | ||||
Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | DAG; 600.155; 302.105; | Approved | no | ||
Call Number | Admin @ si @ BMG2022 | Serial | 3663 | ||
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Author | Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal | ||||
Title | DocEnTr: An End-to-End Document Image Enhancement Transformer | Type | Conference Article | ||
Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1699-1705 | ||
Keywords | Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads | ||||
Abstract | Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR | ||||
Address | August 21-25, 2022 , Montréal Québec | ||||
Corporate Author | Thesis | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
Call Number | Admin @ si @ SBJ2022 | Serial | 3730 | ||
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Author | Kai Wang; Xialei Liu; Andrew Bagdanov; Luis Herranz; Shangling Jui; Joost Van de Weijer | ||||
Title | Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) | Abbreviated Journal | |
Volume | Issue | Pages | 3728-3738 | ||
Keywords | Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis | ||||
Abstract | In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively. |
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Address | New Orleans, USA; 20 June 2022 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WLB2022 | Serial | 3686 | ||
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Author | Zhaocheng Liu; Luis Herranz; Fei Yang; Saiping Zhang; Shuai Wan; Marta Mrak; Marc Gorriz | ||||
Title | Slimmable Video Codec | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop and Challenge on Learned Image Compression (CLIC 2022, 5th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | 1742-1746 | ||
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Abstract | Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression. | ||||
Address | Virtual; 19 June 2022 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MACO; 601.379; 601.161 | Approved | no | ||
Call Number | Admin @ si @ LHY2022 | Serial | 3687 | ||
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Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla | ||||
Title | Human Pose Estimation through a Novel Multi-view Scheme | Type | Conference Article | ||
Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
Volume | 5 | Issue | Pages | 855-862 | |
Keywords | Multi-view Scheme; Human Pose Estimation; Relative Camera Pose; Monocular Approach | ||||
Abstract | This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations. |
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Address | On line; Feb 6, 2022 – Feb 8, 2022 | ||||
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ISSN | 2184-4321 | ISBN | 978-989-758-555-5 | Medium | |
Area | Expedition | Conference | VISAPP | ||
Notes | MSIAU; 600.160 | Approved | no | ||
Call Number | Admin @ si @ CSV2022 | Serial | 3689 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
Title | Multi-Image Super-Resolution for Thermal Images | Type | Conference Article | ||
Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 635-642 | |
Keywords | Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block | ||||
Abstract | This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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Address | Online; Feb 6-8, 2022 | ||||
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Area | Expedition | Conference | VISAPP | ||
Notes | MSIAU; 601.349 | Approved | no | ||
Call Number | Admin @ si @ RSV2022a | Serial | 3690 | ||
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Author | Mohamed Ramzy Ibrahim; Robert Benavente; Felipe Lumbreras; Daniel Ponsa | ||||
Title | 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition | ||||
Abstract | The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively. | ||||
Address | New Orleans, USA; 19 June 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | MSIAU; 600.130 | Approved | no | ||
Call Number | Admin @ si @ IBL2022 | Serial | 3693 | ||
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Author | Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
Title | A Generic Image Retrieval Method for Date Estimation of Historical Document Collections | Type | Conference Article | ||
Year | 2022 | Publication | Document Analysis Systems.15th IAPR International Workshop, (DAS2022) | Abbreviated Journal | |
Volume | 13237 | Issue | Pages | 583–597 | |
Keywords | Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG | ||||
Abstract | Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. | ||||
Address | La Rochelle, France; May 22–25, 2022 | ||||
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Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MGR2022 | Serial | 3694 | ||
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Author | Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A Multilingual Approach to Scene Text Visual Question Answering | Type | Conference Article | ||
Year | 2022 | Publication | Document Analysis Systems.15th IAPR International Workshop, (DAS2022) | Abbreviated Journal | |
Volume | Issue | Pages | 65-79 | ||
Keywords | Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning | ||||
Abstract | Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines. | ||||
Address | La Rochelle, France; May 22–25, 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 611.004; 600.155; 601.002 | Approved | no | ||
Call Number | Admin @ si @ BGK2022b | Serial | 3695 | ||
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