Explore the words cloud of the RSM project. It provides you a very rough idea of what is the project "RSM" about.
The following table provides information about the project.
Coordinator |
RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG
Organization address contact info |
Coordinator Country | Germany [DE] |
Project website | https://hci.iwr.uni-heidelberg.de/vislearn/ |
Total cost | 1˙998˙281 € |
EC max contribution | 1˙998˙281 € (100%) |
Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
Code Call | ERC-2014-CoG |
Funding Scheme | ERC-COG |
Starting year | 2015 |
Duration (year-month-day) | from 2015-10-01 to 2020-09-30 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
1 | RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG | DE (HEIDELBERG) | coordinator | 1˙549˙535.00 |
2 | TECHNISCHE UNIVERSITAET DRESDEN | DE (DRESDEN) | participant | 448˙745.00 |
Computer vision has gained considerable momentum in recent years – both in industry and academia. There seems to be a spirit that the time is ripe to realize grand goals and to bring computer vision from the lab into real life. But is a vision system already as good as a human is? The answer is: “Unfortunately, not yet.” Given a single image, a child can describe the objects and their relationships in a much more detailed manner than any computer can. Also, humans can quite effortlessly “visually extract” an object from its background, even in the presence of fine details such as hair. Computers cannot yet achieve this automatically. But, for many real-world applications it is absolutely necessary to reach such levels of rich output, accuracy, quality, robustness, and system autonomy. In this proposal we try to get closer to this overarching goal. We believe that the key to success is a richer representation. Here “rich” stands for rich, detailed output, modelling rich, physical and semantic constraints, and learning rich, statistical relations between different aspects of a scene. Towards this end we propose the Rich Scene Model (RSM), which is one joint statistical, structured model of many physical and semantic scene aspects that can take full advantage of the synergy effect between all its components. This effort goes beyond previous attempts, in many respects. However, it is simple to say “We will build the best ever joint, rich scene model”. Accordingly, the crux of this proposal is to design novel models, learning and inference techniques to make the RSM a reality. This proposal addresses not only theoretical questions such as, “What can we infer from a few images of a dynamically changing 3D scene?”, and “Is our RSM rich enough to make statistical learning “work better” than deterministic learning?” we also propose a model that can give new forms of output, better deal with challenging real world scenarios, and can adapt nicely to human and application needs
year | authors and title | journal | last update |
---|---|---|---|
2018 |
Anurag Arnab, Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Mans Larsson, Alexander Kirillov, Bogdan Savchynskyy, Carsten Rother, Fredrik Kahl, Philip H.S. Torr Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction published pages: 37-52, ISSN: 1053-5888, DOI: 10.1109/MSP.2017.2762355 |
IEEE Signal Processing Magazine 35/1 | 2019-06-06 |
2018 |
Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars Mescheder, Andreas Geiger, Carsten Rother Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes published pages: , ISSN: 0920-5691, DOI: 10.1007/s11263-018-1070-x |
International Journal of Computer Vision | 2019-06-06 |
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The information about "RSM" are provided by the European Opendata Portal: CORDIS opendata.
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