32,846 research outputs found
Dilated Deep Residual Network for Image Denoising
Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoising have many layers. In such cases, the models involve a large
amount of parameters and are computationally expensive to train. In this paper,
we develop a dilated residual CNN for Gaussian image denoising. Compared with
the recently proposed residual denoiser, our method can achieve comparable
performance with less computational cost. Specifically, we enlarge receptive
field by adopting dilated convolution in residual network, and the dilation
factor is set to a certain value. We utilize appropriate zero padding to make
the dimension of the output the same as the input. It has been proven that the
expansion of receptive field can boost the CNN performance in image
classification, and we further demonstrate that it can also lead to competitive
performance for denoising problem. Moreover, we present a formula to calculate
receptive field size when dilated convolution is incorporated. Thus, the change
of receptive field can be interpreted mathematically. To validate the efficacy
of our approach, we conduct extensive experiments for both gray and color image
denoising with specific or randomized noise levels. Both of the quantitative
measurements and the visual results of denoising are promising comparing with
state-of-the-art baselines.Comment: camera ready, 8 pages, accepted to IEEE ICTAI 201
Discriminative Scale Space Tracking
Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This paper investigates the problem of accurate and robust scale
estimation in a tracking-by-detection framework. We propose a novel scale
adaptive tracking approach by learning separate discriminative correlation
filters for translation and scale estimation. The explicit scale filter is
learned online using the target appearance sampled at a set of different
scales. Contrary to standard approaches, our method directly learns the
appearance change induced by variations in the target scale. Additionally, we
investigate strategies to reduce the computational cost of our approach.
Extensive experiments are performed on the OTB and the VOT2014 datasets.
Compared to the standard exhaustive scale search, our approach achieves a gain
of 2.5% in average overlap precision on the OTB dataset. Additionally, our
method is computationally efficient, operating at a 50% higher frame rate
compared to the exhaustive scale search. Our method obtains the top rank in
performance by outperforming 19 state-of-the-art trackers on OTB and 37
state-of-the-art trackers on VOT2014.Comment: To appear in TPAMI. This is the journal extension of the
VOT2014-winning DSST tracking metho
Deep Anchored Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have been proven to be extremely
successful at solving computer vision tasks. State-of-the-art methods favor
such deep network architectures for its accuracy performance, with the cost of
having massive number of parameters and high weights redundancy. Previous works
have studied how to prune such CNNs weights. In this paper, we go to another
extreme and analyze the performance of a network stacked with a single
convolution kernel across layers, as well as other weights sharing techniques.
We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same
kernel weights across layers allows to reduce the model size tremendously, more
precisely, the network is compressed in memory by a factor of L, where L is the
desired depth of the network, disregarding the fully connected layer for
prediction. The number of parameters in DACNN barely increases as the network
grows deeper, which allows us to build deep DACNNs without any concern about
memory costs. We also introduce a partial shared weights network (DACNN-mix) as
well as an easy-plug-in module, coined regulators, to boost the performance of
our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and
SVHN. Our results show that we can save massive amounts of memory with our
model, while maintaining a high accuracy performance.Comment: This paper is accepted to 2019 IEEE/CVF Conference on Computer Vision
and Pattern Recognition Workshops (CVPRW
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