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Mateusz Pyla, Kamil Deja, Bartłomiej Twardowski, & Tomasz Trzcinski. (2023). Bayesian Flow Networks in Continual Learning.
Abstract: Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data.
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Mathieu Nicolas Delalandre, Ernest Valveny, & Josep Llados. (2008). Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview.
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Mathieu Nicolas Delalandre, Ernest Valveny, & Josep Llados. (2008). Performance Evaluation of Symbol Recognition and Spotting Systems. In Proceedings of the 8th International Workshop on Document Analysis Systems, (497–505).
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Mathieu Nicolas Delalandre, Ernest Valveny, Tony Pridmore, & Dimosthenis Karatzas. (2010). Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems. IJDAR - International Journal on Document Analysis and Recognition, 13(3), 187–207.
Abstract: This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.
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Mathieu Nicolas Delalandre, Jean-Marc Ogier, & Josep Llados. (2007). A Fast System for the Retrieval of Ornamental Letter Image. In Seventh IAPR International Workshop on Graphics Recognition (51–54).
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Mathieu Nicolas Delalandre, Jean-Marc Ogier, & Josep Llados. (2008). A Fast Cbir System of Old Ornamental Letter. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Reognition: Recent Advances and New Opportunities (Vol. 5046, 135–144). LNCS.
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Mathieu Nicolas Delalandre, Jean-Yves Ramel, Ernest Valveny, & Muhammad Muzzamil Luqman. (2009). A Performance Characterization Algorithm for Symbol Localization. In 8th IAPR International Workshop on Graphics Recognition (pp. 3–11). Springer.
Abstract: In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).
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Mathieu Nicolas Delalandre, Jean-Yves Ramel, Ernest Valveny, & Muhammad Muzzamil Luqman. (2010). A Performance Characterization Algorithm for Symbol Localization. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, 260–271). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).
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Mathieu Nicolas Delalandre, Tony Pridmore, Ernest Valveny, Eric Trupin, & Herve Locteau. (2007). Building Synthetic Graphical Documents for Performance Evaluation. In Seventh IAPR International Workshop on Graphics Recognition (84–87).
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Mathieu Nicolas Delalandre, Tony Pridmore, Ernest Valveny, Herve Locteau, & Eric Trupin. (2008). Building Synthetic Graphical Documents for Performance Evaluation. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 288–298). LNCS.
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Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu D. Tizabi, Fabian Isensee, Tim J. Adler, et al. (2023). Why Is the Winner the Best? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 19955–19966).
Abstract: International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
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Matthias S. Keil. (2006). Smooth Gradient Representations as a Unifying Account of Chevreul’s Illusion, Mach Bands, and a Variant of the Ehrenstein Disk. NEURALCOMPUT - Neural Computation, 871–903.
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Matthias S. Keil, Agata Lapedriza, David Masip, & Jordi Vitria. (2008). Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine. PLoS ONE 3(7):e2590, DOI:10.1371/journal.pone.0002590.
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Matthias S. Keil, & Gabriel Cristobal. (2000). Separating the chaff from the wheat: possible origins of the oblique effect. Journal of the Optical Society of America A – Optics, Image Science, and Vision, 17(4): 697–710 (IF: 1.481).
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Matthias S. Keil, Gabriel Cristobal, & Heiko Neumann. (2006). Gradient representation and perception in the early visual system – A novel account of Mach band formation. VR - Vision Research, 46(17): 2659–2674.
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