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Deep Learning Structuré : estimation de pose et reconnaissance de gestes| old_uid | 15573 |
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| title | Deep Learning Structuré : estimation de pose et reconnaissance de gestes |
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| start_date | 2015/04/30 |
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| schedule | 13h30 |
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| online | no |
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| summary | In this talk I will give a short overview over the automatic learning of
deep hierarchical representations including recent advances in this
area. I will briefly cover the basic functionality of frequently used
models such as convolutional neural networks and traditional
applications such as object recognition and video classification.
In the second part of the talk I will address the problem of gesture
recognition and pose estimation from videos, presenting two different
strategies:
(i) estimation of articulated pose (full body or hand pose) alleviates
subsequent recognition steps in many conditions and allows smooth
interaction modes and tight coupling between object and manipulator;
(ii) in situations of low image quality (e.g. large distances between
hand and camera), obtaining an articulated pose is hard. Training a deep
model directly on video data can give excellent results in these situations.
We tackle both cases by training deep architectures capable of learning
discriminative intermediate representations. The main goal is to
integrate structural information into the model in order to decrease the
dependency on large amounts of training data.
- We propose an approach for hand pose estimation that requires very
little labelled data. It leverages both unlabeled data and synthetic
data produced by a rendering pipeline. The key to making it work is to
integrate structural information into the training objective.
- In the context of multi-modal gesture detection and recognition, we
propose a deep recurrent architecture that iteratively learns and
integrates discriminative data representations from individual channels
(pose, video, audio), modeling complex cross-modality correlations and
temporal dependencies. |
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| responsibles | Dutech |
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