The seminars of OBELIX team are currently held on Mondays 11:00 am, every two weeks, at the IRISA lab, Tohannic campus, room D106.
The seminar is coordinated by Chloé Friguet.
Previous seminars – 2019
- Date: February, 11
- Room : D106
- Speaker: Marc Russwurm (Ph.D. student / Univ. Munich, Germany)
- Title: End-to-end Learning for Early Classification of Time Series
- Abstract: Classification of time series is a topical issue in machine learning. While accuracy stands for the most important evaluation criterion, some applications require decisions to be made as early as possible. Optimization should then target a compromise between earliness, i.e., a capacity of providing a decision early in the sequence, and accuracy. During my stay at IRISA-Obelix, I helped designing a generic, end-to-end trainable framework for early classification of time series. This framework embeds a learnable decision mechanism that can be plugged into a wide range of already existing models. We present results obtained with deep neural networks on a diverse set of time series classification problems. The developed approach compares well to state-of-the-art competitors while being easily adaptable by any existing neural network topology that evaluates a hidden state at each time step.
- Date: January, 28
- Room : C011
- Speaker: Behzad Mirmahboub (post-doc / OBELIX-IRISA, Vannes)
- Title: Feature Design and Metric Fusion in Person Re-Identification
- Abstract: Person re-identification is the problem of recognizing a person between several non overlapped cameras. It has important applications in surveillance systems and can reduce human labor and errors of matching persons. Nevertheless, matching two images of a person with different views, poses and illuminations in presence of occlusion and noise is very challenging. Various types of features and metrics are proposed in order to image retrieval for person re-id. In our proposed method we extract different types of features from images and obtain several ranking lists based on their distances. Then, we combine those lists according to their confidence to find the best ranking list.
Reference: Person re-identification by order-induced metric fusion (Neurocomputing 2018)