| Records |
| 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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| Notes |
DAG; 600.155; 600.121 |
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no |
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Admin @ si @ TKV2023 |
Serial |
3825 |
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| Author |
Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades |
| Title |
VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
139 |
Issue |
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Pages |
109419 |
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| Abstract |
Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. |
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ISSN 0031-3203 |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ BMC2023 |
Serial |
3826 |
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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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| Keywords |
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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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no |
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Admin @ si @ TKV2023 |
Serial |
3836 |
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| Author |
Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez |
| Title |
Single image super-resolution based on directional variance attention network |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
133 |
Issue |
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Pages |
108997 |
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| Abstract |
Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. |
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ISE |
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no |
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Admin @ si @ BPF2023 |
Serial |
3861 |
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| Author |
Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia |
| Title |
Dense extreme inception network for edge detection |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
139 |
Issue |
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Pages |
109461 |
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| Abstract |
Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. |
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MSIAU |
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no |
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Admin @ si @ SSH2023 |
Serial |
3982 |
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| Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nikolay; B. Ter Haar Romeny; Juan J. Villanueva; M. Viergerver |
| Title |
New improvements in the multiscale analysis of trabecular bone patterns |
Type |
Book Chapter |
| Year |
2000 |
Publication |
Pattern Recognition and Applications |
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Issue |
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Pages |
251-260 |
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IOS Press |
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ADAS |
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no |
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Admin @ si @ |
Serial |
3418 |
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| Author |
Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
| Title |
Visual behaviours for binocular navigation with autonomous systems. |
Type |
Miscellaneous |
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2000 |
Publication |
Pattern Recognition and Applications, IOS Press, 134–143. |
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| Notes |
OR;ISE;CIC;MV |
Approved |
no |
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BCNPCL @ bcnpcl @ RVV2000 |
Serial |
245 |
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| Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
| Title |
New improvements in the multiscale analysis of trabecular bone patterns. |
Type |
Miscellaneous |
| Year |
2000 |
Publication |
Pattern Recognition and Applications, IOS Press, 251–260. |
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ADAS |
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no |
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ADAS @ adas @ LNS2000 |
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332 |
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| Author |
Daniel Ponsa; A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; Jordi Vitria |
| Title |
Regularized EM. |
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Miscellaneous |
| Year |
2000 |
Publication |
Pattern Recognition and Applications, IOS Press, 69–77. |
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invisible;ADAS;OR;MILAB;MV |
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no |
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ADAS @ adas @ PSL2000 |
Serial |
336 |
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| Author |
Javier Varona; A. Pujol; Juan J. Villanueva |
| Title |
Visual Tracking in Application Domains. |
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Miscellaneous |
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2000 |
Publication |
Pattern Recognition and Applications, IOS Press, 99–106. |
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ISE @ ise @ VPV2000 |
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333 |
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| Author |
Quan-sen Sun; Zhong Jin; Pheng-ann Heng; De-shen Xia |
| Title |
A novel feature fusion method based on partial least squares regression |
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2005 |
Publication |
Pattern Recognition and Data Mining, Lecture Notes in Computer Science, 3686: 268–277 |
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Bath (United Kingdom) |
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no |
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Admin @ si @ SJH2005 |
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626 |
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| Author |
Agnes Borras; Josep Llados |
| Title |
Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints |
Type |
Book Chapter |
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2005 |
Publication |
Pattern Recognition And Image Analysis |
Abbreviated Journal |
LNCS |
| Volume |
3522 |
Issue |
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Pages |
325–332 |
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This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling. |
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Estoril (Portugal) |
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Springer Link |
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DAG; |
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DAG @ dag @ BoL2005; IAM @ iam @ BoL2005 |
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556 |
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| Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
| Title |
Adaptive Correlation Filters for Pattern Recognition |
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Journal |
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2006 |
Publication |
Pattern Recognition and Image Analysis |
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16 |
Issue |
3 |
Pages |
425-431 |
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Pattern recognition, Correlation filters, A adaptive filters |
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Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. |
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ISE |
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no |
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ISE @ ise @ KMA2006a |
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673 |
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| Author |
Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
| Title |
Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System |
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2009 |
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Pattern Recognition and Image Analysis |
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19 |
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1 |
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165-171 |
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An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path. |
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1054-6618 |
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ISE |
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no |
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ISE @ ise @ MAR2009a |
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1160 |
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| Author |
E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
| Title |
Regularized Clustering for Egocentric Video Segmentation |
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Book Chapter |
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2015 |
Publication |
Pattern Recognition and Image Analysis |
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327-336 |
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Temporal video segmentation ; Egocentric videos ; Clustering |
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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Springer International Publishing |
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LNCS |
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978-3-319-19390-8 |
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MILAB |
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no |
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Admin @ si @TDB2015a |
Serial |
2781 |
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