Budget-aware Semi-Supervised Semantic and Instance Segmentation

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.Comment: To appear in CVPR-W 2019 (DeepVision workshop

RVOS: end-to-end recurrent network for video object segmentation

Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence. In our work, we propose a Recurrent network for multiple object Video Object Segmentation (RVOS) that is fully end-to-end trainable. Our model incorporates recurrence on two different domains: (i) the spatial, which allows to discover the different object instances within a frame, and (ii) the temporal, which allows to keep the coherence of the segmented objects along time. We train RVOS for zero-shot video object segmentation and are the first ones to report quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we adapt RVOS for one-shot video object segmentation by using the masks obtained in previous time steps as inputs to be processed by the recurrent module. Our model reaches comparable results to state-of-the-art techniques in YouTube-VOS benchmark and outperforms all previous video object segmentation methods not using online learning in the DAVIS-2017 benchmark. Moreover, our model achieves faster inference runtimes than previous methods, reaching 44ms/frame on a P100 GPU.Peer ReviewedPostprint (published version

Mask-guided sample selection for Semi-Supervised Instance Segmentation

Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. The most common solution to address this constraint is to implement weakly-supervised pipelines trained with lower forms of supervision, such as bounding boxes or scribbles. Another option are semi-supervised methods, which leverage a large amount of unlabeled data and a limited number of strongly-labeled samples. In this second setup, samples to be strongly-annotated can be selected randomly or with an active learning mechanism that chooses the ones that will maximize the model performance. In this work, we propose a sample selection approach to decide which samples to annotate for semi-supervised instance segmentation. Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask. This score is an estimate of the Intersection Over Union (IoU) of the segment with the ground truth mask. We study which samples are better to annotate given the quality score, and show how our approach outperforms a random selection, leading to improved performance for semi-supervised instance segmentation with low annotation budgets.Comment: Preprint submitted to Multimedia Tools and Application

Budget-aware semi-supervised semantic and instance segmentation

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised ap- proaches by considering the total annotation budget, thus allowing a fairer comparison between methods.Peer ReviewedPostprint (author's final draft

Redefining Education in Sports Sciences: A Theoretical Study for Integrating Competency-Based Learning for Sustainable Employment in Spain

In the Spanish context, Sports Sciences education is evolving to emphasize competency-based learning, crucial for adapting to the dynamic global landscape and labor market. This opinion article highlights the shift towards integrating generic and specific competencies, essential for automation and artificial intelligence, aligning with Sustainable Development Goal (SDG) 8’s focus on sustainable economic growth and employment. Despite the recognized importance of these competencies for economic sustainability and job readiness, the literature on this framework, particularly within the context of physical activity and Sports Sciences in Spain, remains underexplored. This paper is structured to first address the current state of the problem, followed by a conceptualization of competencies, including types of competencies. It then analyzes professional competencies within the realm of Physical Activity and Sports Sciences in Spain, moving towards the implementation and evaluation of these competencies in the classroom setting. By bridging the gap between educational outcomes and market demands, this work calls for ongoing research and pedagogical innovation to equip future professionals with the necessary skills for success. This approach not only prepares students for the future labor market but also contributes to the broader economic and sustainable development goals envisioned by SDG 8

RVOS: End-to-End Recurrent Network for Video Object Segmentation

Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence. In our work, we propose a Recurrent network for multiple object Video Object Segmentation (RVOS) that is fully end-to-end trainable. Our model incorporates recurrence on two different domains: (i) the spatial, which allows to discover the different object instances within a frame, and (ii) the temporal, which allows to keep the coherence of the segmented objects along time. We train RVOS for zero-shot video object segmentation and are the first ones to report quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we adapt RVOS for one-shot video object segmentation by using the masks obtained in previous time steps as inputs to be processed by the recurrent module. Our model reaches comparable results to state-of-the-art techniques in YouTube-VOS benchmark and outperforms all previous video object segmentation methods not using online learning in the DAVIS-2017 benchmark. Moreover, our model achieves faster inference runtimes than previous methods, reaching 44ms/frame on a P100 GPU.Comment: CVPR 2019 camera ready. Project website: https://imatge-upc.github.io/rvos

RVOS: end-to-end recurrent network for video object segmentation

Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence. In our work, we propose a Recurrent network for multiple object Video Object Segmentation (RVOS) that is fully end-to-end trainable. Our model incorporates recurrence on two different domains: (i) the spatial, which allows to discover the different object instances within a frame, and (ii) the temporal, which allows to keep the coherence of the segmented objects along time. We train RVOS for zero-shot video object segmentation and are the first ones to report quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we adapt RVOS for one-shot video object segmentation by using the masks obtained in previous time steps as inputs to be processed by the recurrent module. Our model reaches comparable results to state-of-the-art techniques in YouTube-VOS benchmark and outperforms all previous video object segmentation methods not using online learning in the DAVIS-2017 benchmark. Moreover, our model achieves faster inference runtimes than previous methods, reaching 44ms/frame on a P100 GPU.This research was supported by the Spanish Ministry ofEconomy and Competitiveness and the European RegionalDevelopment Fund (TIN2015-66951-C2-2-R, TIN2015-65316-P & TEC2016-75976-R), the BSC-CNS SeveroOchoa SEV-2015-0493 and LaCaixa-Severo Ochoa Inter-national Doctoral Fellowship programs, the 2017 SGR 1414and the Industrial Doctorates 2017-DI-064 & 2017-DI-028from the Government of CataloniaPeer ReviewedPostprint (published version

Guía de actuación para el farmacéutico comunitario en pacientes con hipertensión arterial y riesgo cardiovascular: Documento de consenso

La hipertensión arterial (HTA) es un problema de salud estrechamente relacionado con un aumento del riesgo de padecer una enfermedad cardiovascular. Además, la HTA puede producir o empeorar la lesión de ciertos órganos diana, lo que también puede influir negativamente en el pronóstico cardiovascular del paciente. En España, la HTA es una condición muy frecuente, afectando a unos diez millones de sujetos adultos. Por su accesibilidad y formación especializada en materia de medicamentos, el farmacéutico comunitario puede jugar un papel clave en la detección y seguimiento del paciente con HTA. Hasta la fecha, se han publicado numerosas guías clínicas sobre la atención a pacientes con HTA, dirigidas principalmente a médicos. Sin embargo, cada vez es más evidente la necesidad de que todos los profesionales de la salud participen en la atención integral a los pacientes con HTA y riesgo cardiovascular (RCV). La cooperación entre farmacéutico, médico, personal de enfermería y otros profesionales sanitarios es imprescindible para conseguir resultados que optimicen la prevención cardiovascular y mejoren la calidad de vida del paciente. Así, a fin de promover la gestión compartida de los pacientes con HTA y RCV se publica este documento, cuyo principal destinatario es el farmacéutico comunitario. El presente documento pretende ser una herramienta de referencia que dé soporte a los programas de atención farmacéutica al paciente con HTA y RCV que se están desarrollando actualmente en las oficinas de farmacia. El texto ha sido desarrollado de forma consensuada entre expertos de la Sociedad Española de Hipertensión-Liga Española para la Lucha contra la Hipertensión Arterial (SEH-LELHA), la Sociedad Española de Farmacia Comunitaria (SEFAC) y el Grupo de Investigación en Atención Farmacéutica de la Universidad de Granada (GIAF-UGR