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Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
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Title |
Learning Graph Edit Distance by Graph NeuralNetworks |
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2020 |
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Arxiv |
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The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. |
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DAG; 600.121; 600.140; 601.302 |
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Admin @ si @ RFL2020 |
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3555 |
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Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados |
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Title |
Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild |
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2021 |
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This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild. |
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DAG; 600.121 |
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Admin @ si @ RDB2021 |
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3674 |
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Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
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SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
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2018 |
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Arxiv |
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Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDT2018 |
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3085 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
A probabilistic framework for handwritten text line segmentation |
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2018 |
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Arxiv |
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Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning |
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We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step. |
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DAG; 600.097; 600.121 |
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Admin @ si @ CrR2018 |
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3253 |
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Author |
Y. Patel; Lluis Gomez; Raul Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces |
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2018 |
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Arxiv |
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The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. |
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DAG; 600.084; 601.338; 600.121 |
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Admin @ si @ PGG2018 |
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3177 |
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Author |
Josep Llados; J. Lopez-Krahe; Enric Marti |
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Title |
A Hough-based method for hatched pattern detection in maps and diagrams. |
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1999 |
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Proceedings of the International Conference on Document Analysis and Recognition. |
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Bangalore-India |
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DAG @ dag @ LlM1999b |
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1 |
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Author |
Josep Llados; Gemma Sanchez; Enric Marti |
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Title |
A String-Based Method to Recognize Symbols and Structural Textures in Architectural Plans. |
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1997 |
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Second IAPR Workshop on Graphics Recognition, pp. 287–294. |
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DAG @ dag @ LSM1997 |
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44 |
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Author |
V. Chapaprieta; Ernest Valveny |
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Title |
Handwritten Digit Recognition Using Point Distribution Models. |
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2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:49–54. |
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DAG @ dag @ ChV2001 |
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83 |
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Gemma Sanchez; Josep Llados |
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A Graph Grammar to Recognize Textured Symbols. |
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2001 |
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Sixth International Conference on Document Analysis and Recognition, ICDAR 2001, 465–469. |
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DAG @ dag @ SLl2001 |
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162 |
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Gemma Sanchez; Josep Llados; K. Tombre |
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An Algorithm to Recognize Graphical Textured Symbols using String Representations. |
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2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, :203–208. |
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DAG @ dag @ SLT2001a |
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163 |
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Gemma Sanchez; Josep Llados; K. Tombre |
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An Error-Correction Graph Grammar to Recognize Textured Symbols. |
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2001 |
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Fourth IAPR International Workshop on Graphics Recognition, GREC 2001, 135–146. |
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Canada |
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Josep Llados; Enric Marti |
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A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems. |
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1999 |
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Machine Graphics & Vision, 8(2):195–211. |
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Josep Llados; Gemma Sanchez; K. Tombre |
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An Error-Correction Graph Grammar to Recognize Texture Symbols. |
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2002 |
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Graphics Recognition: Algorithms and Apllications, LNCS 2390: 128–138, Springer Verlag. |
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DAG @ dag @ LST2002 |
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281 |
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Ernest Valveny; B. Lamiroy |
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Automatic Generation of Browsable Technical Documents. |
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2002 |
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Proceedings of the Sixteenth International Conference on Pattern Recognition ICPR 2002: 188–191. |
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DAG @ dag @ VaL2002 |
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301 |
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Philippe Dosch; Josep Llados |
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Vectorial Signatures for Symbol Discrimination |
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2003 |
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Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 |
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Barcelona |
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