271 research outputs found
OnionNet: Sharing Features in Cascaded Deep Classifiers
The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages may add both new layers as well as new feature channels to the previous
ones. Importantly, intermediate feature maps are shared among classifiers,
preventing them from the necessity of being recomputed. To accomplish this, the
model is trained end-to-end in a principled way under a joint loss. We validate
our approach in theory and on a synthetic benchmark. As a result demonstrated
in three applications (patch matching, object detection, and image retrieval),
our cascade can operate significantly faster than both monolithic networks and
traditional cascades without sharing at the cost of marginal decrease in
precision.Comment: Accepted to BMVC 201
Object detection via a multi-region & semantic segmentation-aware CNN model
We propose an object detection system that relies on a multi-region deep
convolutional neural network (CNN) that also encodes semantic
segmentation-aware features. The resulting CNN-based representation aims at
capturing a diverse set of discriminative appearance factors and exhibits
localization sensitivity that is essential for accurate object localization. We
exploit the above properties of our recognition module by integrating it on an
iterative localization mechanism that alternates between scoring a box proposal
and refining its location with a deep CNN regression model. Thanks to the
efficient use of our modules, we detect objects with very high localization
accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we
achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published
work by a significant margin.Comment: Extended technical report -- short version to appear at ICCV 201
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
The problem of computing category agnostic bounding box proposals is utilized
as a core component in many computer vision tasks and thus has lately attracted
a lot of attention. In this work we propose a new approach to tackle this
problem that is based on an active strategy for generating box proposals that
starts from a set of seed boxes, which are uniformly distributed on the image,
and then progressively moves its attention on the promising image areas where
it is more likely to discover well localized bounding box proposals. We call
our approach AttractioNet and a core component of it is a CNN-based category
agnostic object location refinement module that is capable of yielding accurate
and robust bounding box predictions regardless of the object category.
We extensively evaluate our AttractioNet approach on several image datasets
(i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on
all of them state-of-the-art results that surpass the previous work in the
field by a significant margin and also providing strong empirical evidence that
our approach is capable to generalize to unseen categories. Furthermore, we
evaluate our AttractioNet proposals in the context of the object detection task
using a VGG16-Net based detector and the achieved detection performance on COCO
manages to significantly surpass all other VGG16-Net based detectors while even
being competitive with a heavily tuned ResNet-101 based detector. Code as well
as box proposals computed for several datasets are available at::
https://github.com/gidariss/AttractioNet.Comment: Technical report. Code as well as box proposals computed for several
datasets are available at:: https://github.com/gidariss/AttractioNe
LocNet: Improving Localization Accuracy for Object Detection
We propose a novel object localization methodology with the purpose of
boosting the localization accuracy of state-of-the-art object detection
systems. Our model, given a search region, aims at returning the bounding box
of an object of interest inside this region. To accomplish its goal, it relies
on assigning conditional probabilities to each row and column of this region,
where these probabilities provide useful information regarding the location of
the boundaries of the object inside the search region and allow the accurate
inference of the object bounding box under a simple probabilistic framework.
For implementing our localization model, we make use of a convolutional
neural network architecture that is properly adapted for this task, called
LocNet. We show experimentally that LocNet achieves a very significant
improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and
that it can be very easily coupled with recent state-of-the-art object
detection systems, helping them to boost their performance. Finally, we
demonstrate that our detection approach can achieve high detection accuracy
even when it is given as input a set of sliding windows, thus proving that it
is independent of box proposal methods.Comment: Extended technical report -- short version to appear as oral paper on
CVPR 2016. Code: https://github.com/gidariss/LocNet
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
A MAP-Estimation Framework for Blind Deblurring Using High-Level Edge Priors
International audienceIn this paper we propose a general MAP-estimation framework for blind image deconvolution that allows the incorporation of powerful priors regarding predicting the edges of the latent image, which is known to be a crucial factor for the success of blind deblurring. This is achieved in a principled, robust and unified manner through the use of a global energy function that can take into account multiple constraints. Based on this framework, we show how to successfully make use of a particular prior of this type that is quite strong and also applicable to a wide variety of cases. It relates to the strong structural regularity that is exhibited by many scenes, and which affects the location and distribution of the corresponding image edges. We validate the excellent performance of our approach through an extensive set of experimental results and comparisons to the state-of-the-art
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