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Santiago Segui; Michal Drozdzal; Petia Radeva; Jordi Vitria |
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Severe Motility Diagnosis using WCE |
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Conference Article |
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2010 |
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Medical Image Computing in Catalunya: Graduate Student Workshop |
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45–46 |
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Girona, Spain |
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MICCAT |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ SDR2010 |
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1478 |
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Author |
Xavier Otazu; Maria Vanrell |
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Several lightness induction effects with a computational multiresolution wavelet framework |
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2006 |
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29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56 |
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Saint-Petersburg (Russia) |
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CIC |
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CAT @ cat @ OtV2006 |
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659 |
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Josep Llados; W. Liu; Jean-Marc Ogier |
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Seventh IAPR International Workshop on Graphics Recognition GREC 2007 |
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2007 |
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Curitiba (Brazil) |
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DAG |
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DAG @ dag @ LLO2007 |
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835 |
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David Fernandez; R.Manmatha; Josep Llados; Alicia Fornes |
![download PDF file pdf](img/file_PDF.gif)
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Sequential Word Spotting in Historical Handwritten Documents |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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101 - 105 |
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In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a
sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered
index pages of family names. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 600.061; 600.056; 602.006; 600.077 |
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Admin @ si @ FML2014 |
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2462 |
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Author |
Michal Drozdzal |
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Sequential image analysis for computer-aided wireless endoscopy |
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2014 |
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PhD Thesis, Universitat de Barcelona-CVC |
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Wireless Capsule Endoscopy (WCE) is a technique for inner-visualization of the entire small intestine and, thus, offers an interesting perspective on intestinal motility. The two major drawbacks of this technique are: 1) huge amount of data acquired by WCE makes the motility analysis tedious and 2) since the capsule is the first tool that offers complete inner-visualization of the small intestine,the exact importance of the observed events is still an open issue. Therefore, in this thesis, a novel computer-aided system for intestinal motility analysis is presented. The goal of the system is to provide an easily-comprehensible visual description of motility-related intestinal events to a physician. In order to do so, several tools based either on computer vision concepts or on machine learning techniques are presented. A method for transforming 3D video signal to a holistic image of intestinal motility, called motility bar, is proposed. The method calculates the optimal mapping from video into image from the intestinal motility point of view.
To characterize intestinal motility, methods for automatic extraction of motility information from WCE are presented. Two of them are based on the motility bar and two of them are based on frame-per-frame analysis. In particular, four algorithms dealing with the problems of intestinal contraction detection, lumen size estimation, intestinal content characterization and wrinkle frame detection are proposed and validated. The results of the algorithms are converted into sequential features using an online statistical test. This test is designed to work with multivariate data streams. To this end, we propose a novel formulation of concentration inequality that is introduced into a robust adaptive windowing algorithm for multivariate data streams. The algorithm is used to obtain robust representation of segments with constant intestinal motility activity. The obtained sequential features are shown to be discriminative in the problem of abnormal motility characterization.
Finally, we tackle the problem of efficient labeling. To this end, we incorporate active learning concepts to the problems present in WCE data and propose two approaches. The first one is based the concepts of sequential learning and the second one adapts the partition-based active learning to an error-free labeling scheme. All these steps are sufficient to provide an extensive visual description of intestinal motility that can be used by an expert as decision support system. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Petia Radeva |
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978-84-940902-3-3 |
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MILAB |
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no |
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Admin @ si @ Dro2014 |
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2486 |
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Author |
G. Gasbarri; Matias Bilkis; E. Roda Salichs; J. Calsamiglia |
![download PDF file pdf](img/file_PDF.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Sequential hypothesis testing for continuously-monitored quantum systems |
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2024 |
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Quantum |
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8 |
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1289 |
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We consider a quantum system that is being continuously monitored, giving rise to a measurement signal. From such a stream of data, information needs to be inferred about the underlying system's dynamics. Here we focus on hypothesis testing problems and put forward the usage of sequential strategies where the signal is analyzed in real time, allowing the experiment to be concluded as soon as the underlying hypothesis can be identified with a certified prescribed success probability. We analyze the performance of sequential tests by studying the stopping-time behavior, showing a considerable advantage over currently-used strategies based on a fixed predetermined measurement time. |
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xxxx |
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Admin @ si @ GBR2024 |
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3847 |
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Author |
Matthias S. Keil; Gabriel Cristobal |
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Separating the chaff from the wheat: possible origins of the oblique effect |
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2000 |
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Journal of the Optical Society of America A – Optics, Image Science, and Vision, 17(4): 697–710 (IF: 1.481) |
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Admin @ si @ KeC2000 |
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630 |
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Author |
Jose Luis Alba; A. Pujol; Juan J. Villanueva |
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Separating Geometry from Texture to Improve Face Analysis. |
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Miscellaneous |
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2001 |
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Proceeding ICIP 2001, IEEE International Conference on Image Processing, 2:673–676 |
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Grecia |
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ISE @ ise @ APV2001c |
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71 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes |
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2009 |
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Pattern Recognition Letters |
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PRL |
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30 |
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3 |
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285–297 |
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Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2009a |
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1153 |
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Carles Sanchez; Debora Gil; T. Gache; N. Koufos; Marta Diez-Ferrer; Antoni Rosell |
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SENSA: a System for Endoscopic Stenosis Assessment |
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2016 |
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28th Conference of the international Society for Medical Innovation and Technology |
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Documenting the severity of a static or dynamic Central Airway Obstruction (CAO) is crucial to establish proper diagnosis and treatment, predict possible treatment effects and better follow-up the patients. The subjective visual evaluation of a stenosis during video-bronchoscopy still remains the most common way to assess a CAO in spite of a consensus among experts for a need to standardize all calculations [1].
The Computer Vision Center in cooperation with the «Hospital de Bellvitge», has developed a System for Endoscopic Stenosis Assessment (SENSA), which computes CAO directly by analyzing standard bronchoscopic data without the need of using other imaging tecnologies. |
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Rotterdam; The Netherlands; October 2016 |
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SMIT |
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IAM; |
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Admin @ si @ SGG2016 |
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2942 |
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Anna Esposito; Terry Amorese; Nelson Maldonato; Alessandro Vinciarelli; Maria Ines Torres; Sergio Escalera; Gennaro Cordasco |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Seniors’ ability to decode differently aged facial emotional expressions |
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2020 |
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Faces and Gestures in E-health and welfare workshop |
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716-722 |
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Virtual; November 2020 |
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FGW |
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HUPBA |
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Admin @ si @ EAM2020 |
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3515 |
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Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
![goto web page url](img/www.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
SemiDocSeg: Harnessing Semi-Supervised Learning for Document Layout Analysis |
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2024 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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Document layout analysis; Semi-supervised learning; Co-Occurrence matrix; Instance segmentation; Swin transformer |
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Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach. |
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June 2024 |
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DAG |
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Admin @ si @ BBL2024a |
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4001 |
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Yaxing Wang; Salman Khan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan |
![download PDF file pdf](img/file_PDF.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Semi-supervised Learning for Few-shot Image-to-Image Translation |
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Conference Article |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition |
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In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: this https URL. |
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Virtual; June 2020 |
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CVPR |
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LAMP; 600.120 |
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no |
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Admin @ si @ WKG2020 |
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3486 |
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Author |
Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke |
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Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting |
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2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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49-54 |
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State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches. |
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Bari, Italy |
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10.1109/ICFHR.2012.268 |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
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Admin @ si @ FBF2012 |
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2055 |
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Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim |
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Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 |
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2022 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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390-398 |
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Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers |
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This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. |
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New Orleans; USA; June 2022 |
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CVPRW |
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MSIAU; no menciona |
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no |
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Admin @ si @ RMV2022 |
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3774 |
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