3,230 research outputs found
A Dilated Inception Network for Visual Saliency Prediction
Recently, with the advent of deep convolutional neural networks (DCNN), the
improvements in visual saliency prediction research are impressive. One
possible direction to approach the next improvement is to fully characterize
the multi-scale saliency-influential factors with a computationally-friendly
module in DCNN architectures. In this work, we proposed an end-to-end dilated
inception network (DINet) for visual saliency prediction. It captures
multi-scale contextual features effectively with very limited extra parameters.
Instead of utilizing parallel standard convolutions with different kernel sizes
as the existing inception module, our proposed dilated inception module (DIM)
uses parallel dilated convolutions with different dilation rates which can
significantly reduce the computation load while enriching the diversity of
receptive fields in feature maps. Moreover, the performance of our saliency
model is further improved by using a set of linear normalization-based
probability distribution distance metrics as loss functions. As such, we can
formulate saliency prediction as a probability distribution prediction task for
global saliency inference instead of a typical pixel-wise regression problem.
Experimental results on several challenging saliency benchmark datasets
demonstrate that our DINet with proposed loss functions can achieve
state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are
available at https://github.com/ysyscool/DINe
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.Comment: Accepted by TPAM
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