| Records |
| Author |
Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui |
| Title |
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data |
Type |
Conference Article |
| Year |
2022 |
Publication |
10th International Conference on Learning Representations |
Abbreviated Journal |
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| Abstract |
Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data.
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID. |
| Address |
Virtual |
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Conference  |
ICLR |
| Notes |
LAMP; 600.147 |
Approved |
no |
| Call Number |
Admin @ si @ WWY2022 |
Serial |
3791 |
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| Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote |
| Title |
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
multi-view; cross-view; semantic segmentation; synthetic dataset |
| Abstract |
We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . |
| Address |
Bordeaux; France; October2022 |
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Conference |
ICIP |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ AWW2022 |
Serial |
3781 |
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| Author |
Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva |
| Title |
Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring |
| Abstract |
Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. |
| Address |
Bordeaux; France; October 2022 |
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ICIP |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ SBC2022 |
Serial |
3789 |
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| Author |
Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji |
| Title |
Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics |
| Abstract |
In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM. |
| Address |
Bordeaux; France; October 2022 |
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Conference |
ICIP |
| Notes |
MACO |
Approved |
no |
| Call Number |
Admin @ si @ ZWM2022 |
Serial |
3790 |
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| Author |
Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera |
| Title |
Multi-varied Cumulative Alignment for Domain Adaptation |
Type |
Conference Article |
| Year |
2022 |
Publication |
6th International Conference on Image Analysis and Processing |
Abbreviated Journal |
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| Volume |
13232 |
Issue |
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Pages |
324–334 |
| Keywords |
Domain Adaptation; Computer vision; Neural networks |
| Abstract |
Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca). |
| Address |
Indonesia; October 2022 |
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LNCS |
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Conference |
ICIAP |
| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ OAS2022 |
Serial |
3777 |
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| Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
| Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
Type |
Conference Article |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
Abbreviated Journal |
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| Volume |
13639 |
Issue |
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Pages |
3-12 |
| Keywords |
N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
| Abstract |
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
| Address |
December 04 – 07, 2022; Hyderabad, India |
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LNCS |
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Conference |
ICFHR |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ GBS2022 |
Serial |
3733 |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
Type |
Conference Article |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
Abbreviated Journal |
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| Volume |
13639 |
Issue |
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Pages |
171-184 |
| Keywords |
Object detection; Optical music recognition; Graph neural network |
| Abstract |
During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
| Address |
December 04 – 07, 2022; Hyderabad, India |
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LNCS |
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Conference |
ICFHR |
| Notes |
DAG; 600.162; 600.140; 602.230 |
Approved |
no |
| Call Number |
Admin @ si @ BRF2022b |
Serial |
3740 |
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| Author |
Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) |
| Title |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 |
Type |
Book Whole |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. |
Abbreviated Journal |
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| Volume |
13639 |
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| Address |
ICFHR 2022, Hyderabad, India, December 4–7, 2022 |
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| Publisher |
Springer |
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Editor |
Utkarsh Porwal; Alicia Fornes; Faisal Shafait |
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LNCS |
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ISBN |
978-3-031-21648-0 |
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Conference |
ICFHR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ PFS2022 |
Serial |
3809 |
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| Author |
Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang |
| Title |
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video |
Type |
Conference Article |
| Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
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In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
| Address |
Virtual; May 2022 |
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Conference |
ICASSP |
| Notes |
MACO; 600.161; 601.379 |
Approved |
no |
| Call Number |
Admin @ si @ ZHM2022a |
Serial |
3765 |
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| Author |
Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra |
| Title |
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation |
Type |
Conference Article |
| Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
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Issue |
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Pages |
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| Keywords |
Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding |
| Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
| Address |
Singapore; May 2022 |
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Conference |
ICASSP |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MAD2022 |
Serial |
3767 |
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| Author |
Nil Ballus; Bhalaji Nagarajan; Petia Radeva |
| Title |
Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition |
Type |
Conference Article |
| Year |
2022 |
Publication |
10th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
13256 |
Issue |
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Pages |
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| Keywords |
Self-supervised; Contrastive learning; Food recognition |
| Abstract |
Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations. |
| Address |
Aveiro; Portugal; May 2022 |
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LNCS |
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Conference |
IbPRIA |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BNR2022 |
Serial |
3782 |
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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 |
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Issue |
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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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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 |
Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
| Title |
MUST-VQA: MUltilingual Scene-text VQA |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
345–358 |
| Keywords |
Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
| Abstract |
In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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Conference |
ECCVW |
| Notes |
DAG; 302.105; 600.155; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ VBM2022 |
Serial |
3770 |
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| Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
| Title |
Out-of-Vocabulary Challenge Report |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
359–375 |
| Keywords |
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| Abstract |
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
| Address |
Tel-Aviv; Israel; October 2022 |
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Summary Language |
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Original Title |
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LNCS |
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Edition |
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Expedition |
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Conference |
ECCVW |
| Notes |
DAG; 600.155; 302.105; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ GMB2022 |
Serial |
3771 |
| Permanent link to this record |
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| Author |
Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai |
| Title |
Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks |
Type |
Conference Article |
| Year |
2022 |
Publication |
17th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
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Pages |
329–344 |
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| Abstract |
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. |
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LNCS |
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ISBN |
978-3-031-25068-2 |
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Conference |
ECCV-TiE |
| Notes |
DAG; 600.162; 600.140; 110.312 |
Approved |
no |
| Call Number |
Admin @ si @ GBC2022 |
Serial |
3795 |
| Permanent link to this record |
