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[MTC17] Deformable Part-based Fully Convolutional Network for Object Detection
Conférence Internationale avec comité de lecture : British Machine Vision Conference (BMVC), September 2017, London, UK, Best Paper Award,Mots clés: Deep Learning, Object Detection, Part-Based Model
Résumé:
Existing region-based object detectors are limited to regions with fixed box geometry
to represent objects, even if those are highly non-rectangular. In this paper we introduce
DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects
with deformable parts. Without additional annotations, it learns to focus on discrimina-
tive elements and to align them, and simultaneously brings more invariance for classifi-
cation and geometric information to refine localization. DP-FCN is composed of three
main modules: a Fully Convolutional Network to efficiently maintain spatial resolution,
a deformable part-based RoI pooling layer to optimize positions of parts and build invari-
ance, and a deformation-aware localization module explicitly exploiting displacements
of parts to improve accuracy of bounding box regression. We experimentally validate
our model and show significant gains. DP-FCN achieves state-of-the-art performances
of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only
BibTeX
| @inproceedings { | |||
| MTC17, | |||
| title | = | "{Deformable Part-based Fully Convolutional Network for Object Detection}", | |
| author | = | " T. Mordan and N. Thome and M. Cord and G. Henaff ", | |
| booktitle | = | "{British Machine Vision Conference (BMVC)}", | |
| year | = | 2017, | |
| month | = | "September", | |
| address | = | "London, UK, Best Paper Award", | |
| } | |||
