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Wenlong Deng; Yongli Mou; Takahiro Kashiwa; Sergio Escalera; Kohei Nagai; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
![goto web page url](img/www.gif)
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Title |
Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network |
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Journal Article |
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2020 |
Publication ![sorted by Publication field, descending order (down)](img/sort_desc.gif) |
Automation in Construction |
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AC |
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110 |
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Pages |
102973 |
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Semantic image segmentation; Deep learning |
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Abstract |
Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks. |
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HuPBA; no proj |
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Admin @ si @ DMK2020 |
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3314 |
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Author |
Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification |
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Journal Article |
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2022 |
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Automation in Construction |
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144 |
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104614 |
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Sewer Defect Classification; Vision Transformers; Sinkhorn-Knopp; Convolutional Neural Networks; Closed-Circuit Television; Sewer Inspection |
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A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points. |
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Dec 2022 |
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HuPBA |
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Admin @ si @ BME2022c |
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3780 |
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Maya Dimitrova; Ch. Roumenin; Petia Radeva; David Rotger; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Multimodal Intelligent System for Cardiovascular Diagnosis |
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Miscellaneous |
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2003 |
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Automation and Informatics, any XXXVII, num. 3 |
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MILAB |
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BCNPCL @ bcnpcl @ DRR2003 |
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374 |
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Isabelle Guyon; Lisheng Sun Hosoya; Marc Boulle; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michele Sebag; Alexander R.Statnikov; Wei-Wei Tu; Evelyne Viegas |
![goto web page url](img/www.gif)
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Title |
Analysis of the AutoML Challenge Series 2015-2018. |
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2019 |
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Automated Machine Learning |
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177-219 |
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The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/. |
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Springer |
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SSCML |
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HuPBA; no proj |
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Admin @ si @ GHB2019 |
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3330 |
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Author |
Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Moving Cast Shadows Detection Methods for Video Surveillance Applications |
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Book Chapter |
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2014 |
Publication ![sorted by Publication field, descending order (down)](img/sort_desc.gif) |
Augmented Vision and Reality |
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6 |
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23-47 |
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Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). |
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Springer Berlin Heidelberg |
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2190-5916 |
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978-3-642-37840-9 |
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ISE; 605.203; 600.049; 302.018; 302.012; 600.078 |
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Admin @ si @ AHM2014 |
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2223 |
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A. Richichi; O. Fors; M.T. Merino; Xavier Otazu; J. Nuñez; A. Prades; U. Thiele; D. Perez-Ramirez; F.J. Montojo |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
The Calar Alto lunar occultation program: update and new results |
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2006 |
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Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088 |
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CAT @ cat @ RFM2006a |
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589 |
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O. Fors; A. Richichi; Xavier Otazu; J. Nuñez |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A new wavelet-based approach for the automated treatment of large sets of lunar occultation data |
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2008 |
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Astronomy and Astrohysics |
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480 |
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297–304 |
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CAT @ cat @ FRO2008 |
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Author |
Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
![goto web page url](img/www.gif)
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Real-time quality control of surgical material packaging by artificial vision |
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2005 |
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Assembly Automation |
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IF: 0.061) |
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ADAS @ adas @ LVV2005 |
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552 |
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Author |
Enric Marti; Jordi Vitria; Alberto Sanfeliu |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Reconocimiento de Formas y Análisis de Imágenes |
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Book Whole |
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1998 |
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Asociación Española de Reconocimientos de Formas y Análisis de Imágenes |
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Los sistemas actuales de reconocimiento automático del lenguaje oral se basan en dos etapas básicas de procesado: la parametrización, que extrae la evolución temporal de los parámetros que caracterizan la voz, y el reconocimiento propiamente dicho, que identifica la cadena de palabras de la elocución recibida con ayuda de los modelos que representan el conocimiento adquirido en la etapa de aprendizaje. Tomando como línea divisoria la palabra, dichos modelos son de tipo acústicofonético o gramatical. Los primeros caracterizan las palabras incluidas en el vocabulario de la aplicación o tarea a la que está orientado el sistema de reconocimiento, usando a menudo para ello modelos de unidades de habla de extensión inferior a la palabra, es decir, de unidades subléxicas. Por otro lado, la gramática incluye el conocimiento acerca de las combinaciones permitidas de palabras para formar las frases o su probabilidad. Queda fuera del esquema la denominada comprensión del habla, que utiliza adicionalmente el conocimiento semántico y pragmático para captar el significado de la elocución de entrada al sistema a partir de la cadena (o cadenas alternativas) de palabras que suministra el reconocedor. |
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AERFAI |
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84–922529–4–4 |
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IAM;OR;MV |
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IAM @ iam @ MVS1998 |
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1620 |
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A. Martinez; Jordi Vitria |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Development Plataform for Autonomous Agents. |
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1995 |
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ASI–AA–95 – Practice and Future of Autonomous Agents. |
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Monte Verita, Switzerland. |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1995b |
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Adriana Romero; Petia Radeva; Carlo Gatta |
![download PDF file pdf](img/file_PDF.gif)
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No more meta-parameter tuning in unsupervised sparse feature learning |
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2014 |
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Arxiv |
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CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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MILAB; LAMP; 600.079 |
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Admin @ si @ RRG2014 |
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2471 |
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Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
An Effective Solution to Double Counting Problem in Human Pose Estimation |
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Miscellaneous |
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2015 |
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Arxiv |
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Pose estimation; double counting problem; mix-ture of parts Model |
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The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.
An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015]. |
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ISE; 600.078 |
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Admin @ si @ GHG2015 |
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2590 |
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Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos |
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Miscellaneous |
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2015 |
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Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness. |
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MILAB |
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Admin @ si @ ADR2015b |
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2713 |
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Author |
Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest |
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2016 |
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Domain Adaptation; Pedestrian detection; Random Forest |
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Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. |
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ADAS @ adas @ MVJ2016 |
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2868 |
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Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven |
![download PDF file pdf](img/file_PDF.gif)
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End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks |
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2017 |
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arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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HuPBA; ISE; 600.098; 600.119 |
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Admin @ si @ GGM2017 |
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2932 |
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