11,967 research outputs found
Deformable Part-based Fully Convolutional Network for Object Detection
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 discriminative elements and to
align them, and simultaneously brings more invariance for classification 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 invariance, 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.Comment: Accepted to BMVC 2017 (oral
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are
two widely used tools for visual recognition. They are typically viewed as
distinct approaches: DPMs are graphical models (Markov random fields), while
CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM
can be formulated as a CNN, thus providing a novel synthesis of the two ideas.
Our construction involves unrolling the DPM inference algorithm and mapping
each step to an equivalent (and at times novel) CNN layer. From this
perspective, it becomes natural to replace the standard image features used in
DPM with a learned feature extractor. We call the resulting model DeepPyramid
DPM and experimentally validate it on PASCAL VOC. DeepPyramid DPM significantly
outperforms DPMs based on histograms of oriented gradients features (HOG) and
slightly outperforms a comparable version of the recently introduced R-CNN
detection system, while running an order of magnitude faster
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
We propose a method for predicting the 3D shape of a deformable surface from
a single view. By contrast with previous approaches, we do not need a
pre-registered template of the surface, and our method is robust to the lack of
texture and partial occlusions. At the core of our approach is a {\it
geometry-aware} deep architecture that tackles the problem as usually done in
analytic solutions: first perform 2D detection of the mesh and then estimate a
3D shape that is geometrically consistent with the image. We train this
architecture in an end-to-end manner using a large dataset of synthetic
renderings of shapes under different levels of deformation, material
properties, textures and lighting conditions. We evaluate our approach on a
test split of this dataset and available real benchmarks, consistently
improving state-of-the-art solutions with a significantly lower computational
time.Comment: Accepted at CVPR 201
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