25,764 research outputs found
Exploring Context with Deep Structured models for Semantic Segmentation
State-of-the-art semantic image segmentation methods are mostly based on
training deep convolutional neural networks (CNNs). In this work, we proffer to
improve semantic segmentation with the use of contextual information. In
particular, we explore `patch-patch' context and `patch-background' context in
deep CNNs. We formulate deep structured models by combining CNNs and
Conditional Random Fields (CRFs) for learning the patch-patch context between
image regions. Specifically, we formulate CNN-based pairwise potential
functions to capture semantic correlations between neighboring patches.
Efficient piecewise training of the proposed deep structured model is then
applied in order to avoid repeated expensive CRF inference during the course of
back propagation. For capturing the patch-background context, we show that a
network design with traditional multi-scale image inputs and sliding pyramid
pooling is very effective for improving performance. We perform comprehensive
evaluation of the proposed method. We achieve new state-of-the-art performance
on a number of challenging semantic segmentation datasets including ,
-, , -, -,
-, and datasets. Particularly, we report an
intersection-over-union score of on the - dataset.Comment: 16 pages. Accepted to IEEE T. Pattern Analysis & Machine
Intelligence, 2017. Extended version of arXiv:1504.0101
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.Comment: Accepted by ICCV 201
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