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In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, composit- ing synthetic 3D objects within real scenes. We show the perfor- mance of the proposed system in the context of object detection in thermal videos, a domain where i) training datasets are very limited compared to visible spectrum datasets and ii) creating full realistic synthetic scenes is extremely cumbersome and expensive due to the difficulty in modeling the thermal properties of the materials of the scene. We compare different augmentation strategies, including state of the art approaches obtained through RL techniques, the injection of simulated data and the employment of a generative model, and study how to best combine our proposed augmentation with these other techniques.
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\nZenodo’s general policies and Terms of Use apply to all content.
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", "description": "Open repository for EU-funded research outputs from Horizon Europe, Euratom and earlier Framework Programmes.", "organizations": [ { "id": "00k4n6c32" } ], "page": "The EU Open Research Repository is a Zenodo-community dedicated to fostering open science and enhancing the visibility and accessibility of research outputs funded by the European Union. The community is managed by CERN on behalf of the European Commission.
\nThe mission of the repository is to support the implementation of the EU's open science policy, providing a trusted and comprehensive space for researchers to share their research outputs such as data, software, reports, presentations, posters and more. The EU Open Research Repository simplifies the process of complying with open science requirements, ensuring that research outputs from Horizon Europe, Euratom, and earlier Framework Programmes are freely accessible, thereby accelerating scientific discovery and innovation.
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