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In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover,...
Given an untextured 3D car models dataset, are we able to learn a robust make and model classifier which will be applied on real color images? One solution consists in finding a common representation between synthetic edge images and real color images. To address this issue, we introduce novel edge-color invariant features for 2D/3D car fine-grained classification. These features are learned simultaneously...
Building large video datasets is a crucial task for many applications but is also very expensive in practice. In order to avoid annotating all the frames, the annotations from the labeled frames can be propagated using an offline tracker for each object. Following methods based on dynamic programming and eventually distance transforms, we introduce a new penalization which favors some given displacements...
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