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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier
Title Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
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Abstract Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.
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Notes HUPBA Approved no
Call Number Admin @ si @ JKS2018 Serial (down) 3095
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Author Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal
Title SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
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Abstract 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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Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ DDT2018 Serial (down) 3085
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Author Fernando Vilariño
Title Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation Type Miscellaneous
Year 2017 Publication Open Living Lab Days Report Abbreviated Journal
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Address Krakow; August 2017
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Notes MV; no menciona;SIAI Approved no
Call Number Admin @ si @ Vil2017b Serial (down) 3033
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Author Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven
Title End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks Type Miscellaneous
Year 2017 Publication Arxiv Abbreviated Journal
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Abstract 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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Notes HuPBA; ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ GGM2017 Serial (down) 2932
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Author Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez
Title Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest Type Miscellaneous
Year 2016 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords Domain Adaptation; Pedestrian detection; Random Forest
Abstract 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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Notes ADAS Approved no
Call Number ADAS @ adas @ MVJ2016 Serial (down) 2868
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Author Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers
Title Understanding Road Scenes using Visual Cues Type Miscellaneous
Year 2012 Publication European Conference on Computer Vision Abbreviated Journal
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Abstract DEMO
Address Florence; Italy
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Notes ISE Approved no
Call Number Admin @ si @ ALL2012 Serial (down) 2795
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos Type Miscellaneous
Year 2015 Publication Arxiv Abbreviated Journal
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Abstract 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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Notes MILAB Approved no
Call Number Admin @ si @ ADR2015b Serial (down) 2713
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Author Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang
Title An Effective Solution to Double Counting Problem in Human Pose Estimation Type Miscellaneous
Year 2015 Publication Arxiv Abbreviated Journal
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Keywords Pose estimation; double counting problem; mix-ture of parts Model
Abstract 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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Notes ISE; 600.078 Approved no
Call Number Admin @ si @ GHG2015 Serial (down) 2590
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Author Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez
Title 3d Pedestrian Detection via Random Forest Type Miscellaneous
Year 2014 Publication European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 231-238
Keywords Pedestrian Detection
Abstract Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications.
Address Zurich; suiza; September 2014
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Area Expedition Conference ECCV-Demo
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ VRR2014 Serial (down) 2570
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Author Adriana Romero; Petia Radeva; Carlo Gatta
Title No more meta-parameter tuning in unsupervised sparse feature learning Type Miscellaneous
Year 2014 Publication Arxiv Abbreviated Journal
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Abstract 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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Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ RRG2014 Serial (down) 2471
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil
Title Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías Type Miscellaneous
Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal
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Address Tarragona; juliol 2014
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Area Expedition Conference CIDUI
Notes IAM; 600.075;DAG Approved no
Call Number Admin @ si @ SRM2014 Serial (down) 2458
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Author Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda
Title ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos Type Miscellaneous
Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal
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Address Tarragona; juliol 2014
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Area Expedition Conference CIDUI
Notes IAM; ADAS; 600.076; 600.063; 600.075 Approved no
Call Number Admin @ si @ MGG2014 Serial (down) 2457
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Author Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil; Aura Hernandez-Sabate
Title Una experiencia de estructura, funcionamiento y evaluación de la asignatura de graficos por computador con metodologia de aprendizaje basado en proyectos Type Miscellaneous
Year 2013 Publication IV Congreso Internacional UNIVEST Abbreviated Journal
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Abstract IV Congreso Internacional UNIVEST
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Notes IAM; ADAS Approved no
Call Number Admin @ si @ MPG2013b Serial (down) 2384
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Author Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund
Title Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Type Miscellaneous
Year 2013 Publication ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream Abbreviated Journal
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Address Barcelona; October 2013
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Notes ISE Approved no
Call Number Admin @ si @ DDB2013 Serial (down) 2372
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Author Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil
Title Una propuesta de seguimiento, tutorías on line y evaluación en la metodología de Aprendizaje Basado en Proyectos Type Miscellaneous
Year 2013 Publication IV Congreso Internacional UNIVEST Abbreviated Journal
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Abstract Poster
Address Girona
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Area Expedition Conference UNIVEST
Notes IAM Approved no
Call Number Admin @ si @ MPG2013a Serial (down) 2304
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