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In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. The G-CRF system matrix is therefore low-rank, allowing us to solve the resulting...
Mobility impairment is one of the biggest challenges faced by elderly people in today's society. The inability to move about freely poses severe restrictions on their independence and general quality of life. This work is dedicated to developing intelligent robotic platforms that assist users to move without requiring a human attendant. This work was done in the context of an EU project involved in...
Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, preserving most of the relevant information. Feature learning can...
Computing similarities between data samples is a fundamental step in most Pattern Recognition (PR) tasks. Better similarity measures lead to more accurate prediction of labels. Computing similarities between video sequences has been a challenging problem for the PR community for long because videos have both spatial and temporal context which are hard to capture. We describe a novel approach that...
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