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Patricia Suarez, Angel Sappa, & Boris X. Vintimilla. (2017). Infrared Image Colorization based on a Triplet DCGAN Architecture. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.
Abstract: This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time.
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Hugo Jair Escalante, Isabelle Guyon, Sergio Escalera, Julio C. S. Jacques Junior, Xavier Baro, Evelyne Viegas, et al. (2017). Design of an Explainable Machine Learning Challenge for Video Interviews. In International Joint Conference on Neural Networks.
Abstract: This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration.
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Julio C. S. Jacques Junior, Xavier Baro, & Sergio Escalera. (2017). Exploiting feature representations through similarity learning and ranking aggregation for person re-identification. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: Person re-identification has received special attentionby the human analysis community in the last few years.To address the challenges in this field, many researchers haveproposed different strategies, which basically exploit eithercross-view invariant features or cross-view robust metrics. Inthis work we propose to combine different feature representationsthrough ranking aggregation. Spatial information, whichpotentially benefits the person matching, is represented usinga 2D body model, from which color and texture informationare extracted and combined. We also consider contextualinformation (background and foreground data), automaticallyextracted via Deep Decompositional Network, and the usage ofConvolutional Neural Network (CNN) features. To describe thematching between images we use the polynomial feature map,also taking into account local and global information. Finally,the Stuart ranking aggregation method is employed to combinecomplementary ranking lists obtained from different featurerepresentations. Experimental results demonstrated that weimprove the state-of-the-art on VIPeR and PRID450s datasets,achieving 58.77% and 71.56% on top-1 rank recognitionrate, respectively, as well as obtaining competitive results onCUHK01 dataset.
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Iiris Lusi, Julio C. S. Jacques Junior, Jelena Gorbova, Xavier Baro, Sergio Escalera, Hasan Demirel, et al. (2017). Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
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Chirster Loob, Pejman Rasti, Iiris Lusi, Julio C. S. Jacques Junior, Xavier Baro, Sergio Escalera, et al. (2017). Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions.
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Pau Rodriguez, Jordi Gonzalez, Jordi Cucurull, Josep M. Gonfaus, & Xavier Roca. (2017). Regularizing CNNs with Locally Constrained Decorrelations. In 5th International Conference on Learning Representations.
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Hana Jarraya, Muhammad Muzzamil Luqman, & Jean-Yves Ramel. (2017). Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition. In B. Lamiroy, & R Dueire Lins (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 9657). LNCS. Springer.
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Umut Guclu, Yagmur Gucluturk, Meysam Madadi, Sergio Escalera, Xavier Baro, Jordi Gonzalez, et al. (2017). End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks.
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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H. Martin, Jens Fagertun, Sergio Vera, & Debora Gil. (2017). Medial structure generation for registration of anatomical structures. In Skeletonization, Theory, Methods and Applications (Vol. 11).
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Mireia Sole, Joan Blanco, Debora Gil, Oliver Valero, G. Fonseka, M. Lawrie, et al. (2017). Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study. In 11th European CytoGenesis Conference.
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Marc Bolaños, Alvaro Peris, Francisco Casacuberta, & Petia Radeva. (2017). VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering. In 8th Iberian Conference on Pattern Recognition and Image Analysis.
Abstract: In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
Keywords: Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
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Carles Sanchez, Debora Gil, T. Gache, N. Koufos, Marta Diez-Ferrer, & Antoni Rosell. (2016). SENSA: a System for Endoscopic Stenosis Assessment. In 28th Conference of the international Society for Medical Innovation and Technology.
Abstract: 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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Marta Diez-Ferrer, Debora Gil, Elena Carreño, Susana Padrones, Samantha Aso, Vanesa Vicens, et al. (2017). Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation. ERJ - European Respiratory Journal, .
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Quentin Angermann, Jorge Bernal, Cristina Sanchez Montes, Maroua Hammami, Gloria Fernandez Esparrach, Xavier Dray, et al. (2017). Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis. In 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery.
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Hana Jarraya, Oriol Ramos Terrades, & Josep Llados. (2017). Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs. In 8th Iberian Conference on Pattern Recognition and Image Analysis.
Abstract: We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
Keywords: Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
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