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Guim Perarnau, Joost Van de Weijer, Bogdan Raducanu, & Jose Manuel Alvarez. (2016). Invertible conditional gans for image editing. In 30th Annual Conference on Neural Information Processing Systems Worshops.
Abstract: Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.
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Xavier Baro, Sergio Escalera, Isabelle Guyon, Julio C. S. Jacques Junior, Lukasz Romaszko, Lisheng Sun, et al. (2016). Coompetitions in machine learning: case studies. In 30th Annual Conference on Neural Information Processing Systems Worshops.
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Fernando Alonso, Xavier Baro, Sergio Escalera, Jordi Gonzalez, Martha Mackay, & Anna Serrahima. (2016). CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions. In 16th International Conference for Integrated Care.
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G. de Oliveira, Mariella Dimiccoli, & Petia Radeva. (2016). Egocentric Image Retrieval With Deep Convolutional Neural Networks. In 19th International Conference of the Catalan Association for Artificial Intelligence (pp. 71–76).
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Fernando Vilariño. (2016). Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation. In International Workshop on Poetry: Archives, Poetries and Receptions.
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Jose A. Garcia, David Masip, Valerio Sbragaglia, & Jacopo Aguzzi. (2016). Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light. In 19th International Conference of the Catalan Association for Artificial Intelligence.
Abstract: Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community.
Keywords: computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus
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Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes Garcıa-Martinez, Fethi Bougares, et al. (2016). Does Multimodality Help Human and Machine for Translation and Image Captioning? In 1st conference on machine translation.
Abstract: This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.
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Fernando Vilariño, & Dimosthenis Karatzas. (2016). A Living Lab approach for Citizen Science in Libraries. In 1st International ECSA Conference.
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Fernando Vilariño. (2016). Giving Value to digital collections in the Public Library. In Librarian 2020.
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Jose Ramirez Moreno, Juan R Revilla, Miguel Reyes, & Sergio Escalera. (2016). Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal. In 4th Congreso WCPT-SAR.
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2016). With whom do I interact with? Social interaction detection in egocentric photo-streams. In 23rd International Conference on Pattern Recognition.
Abstract: Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
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Dena Bazazian, Raul Gomez, Anguelos Nicolaou, Lluis Gomez, Dimosthenis Karatzas, & Andrew Bagdanov. (2016). Improving Text Proposals for Scene Images with Fully Convolutional Networks. In 23rd International Conference on Pattern Recognition Workshops.
Abstract: Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
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Hugo Jair Escalante, Victor Ponce, Jun Wan, Michael A. Riegler, Baiyu Chen, Albert Clapes, et al. (2016). ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview. In 23rd International Conference on Pattern Recognition.
Abstract: This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from
short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost
200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to
allow researchers to keep making progress in the four tracks.
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Marc Bolaños, & Petia Radeva. (2016). Simultaneous Food Localization and Recognition. In 23rd International Conference on Pattern Recognition.
Abstract: CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images.
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2016). With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams. In 23rd International Conference on Pattern Recognition.
Abstract: Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
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