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Author | Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez | ||||
Title | Top-down model fitting for hand pose recovery in sequences of depth images | Type | Journal Article | ||
Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 79 | Issue | Pages | 63-75 | |
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Abstract | State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. | ||||
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Notes | HUPBA; 600.098 | Approved | no | ||
Call Number | Admin @ si @ MEC2018 | Serial | 3203 | ||
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Author | Marc Oliu; Javier Selva; Sergio Escalera | ||||
Title | Folded Recurrent Neural Networks for Future Video Prediction | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11218 | Issue | Pages | 745-761 | |
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Abstract | Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach. | ||||
Address | Munich; September 2018 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ECCV | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ OSE2018 | Serial | 3204 | ||
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Author | Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera | ||||
Title | Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues | Type | Conference Article | ||
Year | 2018 | Publication | 29th British Machine Vision Conference | Abbreviated Journal | |
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Abstract | Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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Address | Newcastle; UK; September 2018 | ||||
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Area | Expedition | Conference | BMVC | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ PSB2018 | Serial | 3208 | ||
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Author | Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier | ||||
Title | Multimodal First Impression Analysis with Deep Residual Networks | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 8 | Issue | 3 | Pages | 316-329 |
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Abstract | People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GGB2018 | Serial | 3210 | ||
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Author | Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon | ||||
Title | Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes | Type | Conference Article | ||
Year | 2018 | Publication | Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) | Abbreviated Journal | |
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Address | Beijing; China; August 2018 | ||||
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Area | Expedition | Conference | ICPRW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ RVI2018 | Serial | 3211 | ||
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Author | Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya | ||||
Title | Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” | Type | Journal | ||
Year | 2018 | Publication | Informaciones Psiquiatricas | Abbreviated Journal | |
Volume | 232 | Issue | Pages | 47-59 | |
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ISSN | 0210-7279 | ISBN | Medium | ||
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ FAA2018 | Serial | 3214 | ||
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Author | Suman Ghosh | ||||
Title | Word Spotting and Recognition in Images from Heterogeneous Sources A | Type | Book Whole | ||
Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations. | ||||
Address | November 2018 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny | |
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ISSN | ISBN | 978-84-948531-0-4 | Medium | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ Gho2018 | Serial | 3217 | ||
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Author | Gholamreza Anbarjafari; Sergio Escalera | ||||
Title | Human-Robot Interaction: Theory and Application | Type | Book Whole | ||
Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
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ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ AnE2018 | Serial | 3216 | ||
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Author | Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza | ||||
Title | Deep Learning models for passability detection of flooded roads | Type | Conference Article | ||
Year | 2018 | Publication | MediaEval 2018 Multimedia Benchmark Workshop | Abbreviated Journal | |
Volume | 2283 | Issue | Pages | ||
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Abstract | In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task. | ||||
Address | Sophia Antipolis; France; October 2018 | ||||
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Area | Expedition | Conference | MediaEval | ||
Notes | LAMP; 600.084; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ LFS2018 | Serial | 3224 | ||
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Author | Antonio Lopez | ||||
Title | Pedestrian Detection Systems | Type | Book Chapter | ||
Year | 2018 | Publication | Wiley Encyclopedia of Electrical and Electronics Engineering | Abbreviated Journal | |
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Abstract | Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Lop2018 | Serial | 3230 | ||
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Author | Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo | ||||
Title | 2D-to-3D Facial Expression Transfer | Type | Conference Article | ||
Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2008 - 2013 | ||
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Abstract | Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops. |
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Area | Expedition | Conference | ICPR | ||
Notes | MSIAU; 600.086; 600.130; 600.118 | Approved | no | ||
Call Number | Admin @ si @ RLM2018 | Serial | 3232 | ||
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Author | Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig | ||||
Title | CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ KJR2018 | Serial | 3235 | ||
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Author | Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva | ||||
Title | Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | |
Volume | 20 | Issue | 12 | Pages | 3266 - 3275 |
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Abstract | The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ ARB2018 | Serial | 3236 | ||
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Author | Marçal Rusiñol; Lluis Gomez | ||||
Title | Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos | Type | Journal | ||
Year | 2018 | Publication | Revista anual de la Asociación de Archiveros de Castilla y León | Abbreviated Journal | |
Volume | 21 | Issue | Pages | 161-174 | |
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Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ RuG2018 | Serial | 3239 | ||
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Author | Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer | ||||
Title | LIUM-CVC Submissions for WMT18 Multimodal Translation Task | Type | Conference Article | ||
Year | 2018 | Publication | 3rd Conference on Machine Translation | Abbreviated Journal | |
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Abstract | This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions
ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR. |
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Address | Brussels; Belgium; October 2018 | ||||
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Area | Expedition | Conference | WMT | ||
Notes | LAMP; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ CBB2018 | Serial | 3240 | ||
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