Hugo Prol, Vincent Dumoulin, & Luis Herranz. (2018). Cross-Modulation Networks for Few-Shot Learning.
Abstract: A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art.
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Simeon Petkov, Xavier Carrillo, Petia Radeva, & Carlo Gatta. (2014). Diaphragm border detection in coronary X-ray angiographies: New method and applications. CMIG - Computerized Medical Imaging and Graphics, 38(4), 296–305.
Abstract: X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation.
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Victor Ponce, Baiyu Chen, Marc Oliu, Ciprian Corneanu, Albert Clapes, Isabelle Guyon, et al. (2016). ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results. In 14th European Conference on Computer Vision Workshops.
Abstract: This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge.
Keywords: Behavior Analysis; Personality Traits; First Impressions
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David Pujol Perich, Albert Clapes, & Sergio Escalera. (2023). SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization.
Abstract: Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP.
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Alex Pardo, Albert Clapes, Sergio Escalera, & Oriol Pujol. (2013). Actions in Context: System for people with Dementia. In 2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems (pp. 3–14). Springer International Publishing.
Abstract: In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios.
Keywords: Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia
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Cristina Palmero, Albert Clapes, Chris Bahnsen, Andreas Møgelmose, Thomas B. Moeslund, & Sergio Escalera. (2016). Multi-modal RGB-Depth-Thermal Human Body Segmentation. IJCV - International Journal of Computer Vision, 118(2), 217–239.
Abstract: This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations.
Keywords: Human body segmentation; RGB ; Depth Thermal
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Eloi Puertas, Miguel Angel Bautista, Daniel Sanchez, Sergio Escalera, & Oriol Pujol. (2014). Learning to Segment Humans by Stacking their Body Parts,. In ECCV Workshop on ChaLearn Looking at People (Vol. 8925, pp. 685–697). LNCS.
Abstract: Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body
part likelihood maps. These likelihood maps are obtained in a first stage
by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline.
Keywords: Human body segmentation; Stacked Sequential Learning
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Alvaro Peris, Marc Bolaños, Petia Radeva, & Francisco Casacuberta. (2016). Video Description Using Bidirectional Recurrent Neural Networks. In 25th International Conference on Artificial Neural Networks (Vol. 2, pp. 3–11).
Abstract: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.
Keywords: Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks
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I. Payan. (2001). El uso del recalaje en la construccion de imagenes de superresolucion.
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N. Pares, & J.R. Serra. (1992). Tailleur: El problema del sastre..
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C. Alejandro Parraga. (2015). Perceptual Psychophysics. In G.Cristobal, M.Keil, & L.Perrinet (Eds.), Biologically-Inspired Computer Vision: Fundamentals and Applications.
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C. Alejandro Parraga. (2014). Color Vision, Computational Methods for. In Dieter Jaeger, & Ranu Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–11). Springer-Verlag Berlin Heidelberg.
Abstract: The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.
Keywords: Color computational vision; Computational neuroscience of color
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A. Pagani. (2002). Analisis y comparacion de metodos para la caracterizacion de la respuesta espectral de camaras.
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Andrei Polzounov, Artsiom Ablavatski, Sergio Escalera, Shijian Lu, & Jianfei Cai. (2017). WordFences: Text Localization and Recognition. In 24th International Conference on Image Processing.
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Florin Popescu, Stephane Ayache, Sergio Escalera, Xavier Baro, Cecile Capponi, Patrick Panciatici, et al. (2016). From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning. In European Geosciences Union General Assembly (Vol. 18).
Abstract: The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image.
We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks.
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