5,722 research outputs found
Integrated Deep and Shallow Networks for Salient Object Detection
Deep convolutional neural network (CNN) based salient object detection
methods have achieved state-of-the-art performance and outperform those
unsupervised methods with a wide margin. In this paper, we propose to integrate
deep and unsupervised saliency for salient object detection under a unified
framework. Specifically, our method takes results of unsupervised saliency
(Robust Background Detection, RBD) and normalized color images as inputs, and
directly learns an end-to-end mapping between inputs and the corresponding
saliency maps. The color images are fed into a Fully Convolutional Neural
Networks (FCNN) adapted from semantic segmentation to exploit high-level
semantic cues for salient object detection. Then the results from deep FCNN and
RBD are concatenated to feed into a shallow network to map the concatenated
feature maps to saliency maps. Finally, to obtain a spatially consistent
saliency map with sharp object boundaries, we fuse superpixel level saliency
map at multi-scale. Extensive experimental results on 8 benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches with a margin.Comment: Accepted by IEEE International Conference on Image Processing (ICIP)
201
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
We propose a structured prediction architecture, which exploits the local
generic features extracted by Convolutional Neural Networks and the capacity of
Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed
architecture, called ReSeg, is based on the recently introduced ReNet model for
image classification. We modify and extend it to perform the more challenging
task of semantic segmentation. Each ReNet layer is composed of four RNN that
sweep the image horizontally and vertically in both directions, encoding
patches or activations, and providing relevant global information. Moreover,
ReNet layers are stacked on top of pre-trained convolutional layers, benefiting
from generic local features. Upsampling layers follow ReNet layers to recover
the original image resolution in the final predictions. The proposed ReSeg
architecture is efficient, flexible and suitable for a variety of semantic
segmentation tasks. We evaluate ReSeg on several widely-used semantic
segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving
state-of-the-art performance. Results show that ReSeg can act as a suitable
architecture for semantic segmentation tasks, and may have further applications
in other structured prediction problems. The source code and model
hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201
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