CORDIS Project
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This project explores a novel approach that combines unsupervised visual inference with deep learning to enhance image and video analysis. It aims to leverage internal data patterns while minimizing the need for extensive training datasets, potentially revolutionizing various applications in computer vision.
Unsupervised visual inference can often be performed by exploiting the internal redundancy inside a single visual datum (an image or a video).
The strong repetition of patches inside a single image/video provides a powerful data-specific prior for solving a variety of vision tasks in a “blind” manner: (i) Blind in the sense that sophisticated unsupervised inferences can be made with no prior examples or training; (ii) Blind in the sense that complex ill-posed Inverse-Problems can be solved, even…
WEIZMANN INSTITUTE OF SCIENCE
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