Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations

This paper presents a co-clustering technique that, given a collection of images and their hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection. We formalize the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a linear programming relaxation approach that makes effective use of information from hierarchies. Initially, we address the problem of generating an optimal, coherent partition per image and, afterwards, we extend this method to a multiresolution framework. Finally, we particularize this framework to an iterative multiresolution video segmentation algorithm in sequences with small variations. We evaluate the algorithm on the Video Occlusion/Object Boundary Detection Dataset, showing that it produces state-of-the-art results in these scenarios.Comment: International Conference on Computer Vision (ICCV) 201

Objective evaluation criteria for 2D-shape estimation results of moving objects

The objective evaluation of 2D-shape estimation results for moving objects in a video sequence is still an open problem. First approaches in the literature evaluate the spatial accuracy and the temporal coherency of the estimated 2D object shape. Thereby, it is not distinguished between several estimation errors located around the object contour and a few, but larger, estimation errors. Both cases would lead to similar evaluation results, although the 2D-shapes would be visually very different. To overcome this problem, in this paper, a new evaluation approach is proposed. In it, the evaluation of the spatial accuracy and the temporal coherency is based on the mean and the standard deviation of the 2D-shape estimation errors

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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

Preventing chronic malnutrition in children under 2 years in rural Angola (MuCCUA trial): protocol for the economic evaluation of a three-arm community cluster randomised controlled trial

Chronic malnutrition; Angola; Economic evaluationDesnutrición crónica; Angola; Evaluación económicaDesnutrició crònica; Angola; Avaluació econòmicaIntroduction Chronic malnutrition is a serious problem in southern Angola with a prevalence of 49.9% and 37.2% in the provinces of Huila and Cunene, respectively. The MuCCUA (Mother and Child Chronic Undernutrition in Angola) trial is a community-based randomised controlled trial (c-RCT) which aims to evaluate the effectiveness of a nutrition supplementation plus standard of care intervention and a cash transfer plus standard of care intervention in preventing stunting, and to compare them with a standard of care alone intervention in southern Angola. This protocol describes the planned economic evaluation associated with the c-RCT. Methods and analysis We will conduct a cost-efficiency and cost-effectiveness analysis nested within the MuCCUA trial with a societal perspective, measuring programme, provider, participant and household costs. We will collect programme costs prospectively using a combined calculation method including quantitative and qualitative data. Financial costs will be estimated by applying activity-based costing methods to accounting records using time allocation sheets. We will estimate costs not included in accounting records by the ingredients approach, and indirect costs incurred by beneficiaries through interviews, household surveys and focus group discussions. Cost-efficiency will be estimated as cost per output achieved by combining activity-specific cost data with routine data on programme outputs. Cost-effectiveness will be assessed as cost per stunting case prevented. We will calculate incremental cost-effectiveness ratios comparing the additional cost per improved outcome of the different intervention arms and the standard of care. We will perform sensitivity analyses to assess robustness of results. Ethics and dissemination This economic evaluation will provide useful information to the Angolan Government and other policymakers on the most cost-effective intervention to prevent stunting in this and other comparable contexts. The protocol was approved by the República de Angola Ministério da Saúde Comité de Ética (27C.E/MINSA.INIS/2022). The findings of this study will be disseminated within academia and the wider policy sphere. Trial registration number ClinicalTrials.gov Registry (NCT05571280).The Crescer Project is funded by the European Union EuropeAid contract no FED/2020/418-106 and co-financed by the Crescer consortium partners. The MuCCUA trial is funded by the European Union as a part of the Crescer Project (IV component of the FRESAN Programme) for the 4-year duration of the project