4,951 research outputs found
Visual Saliency Based on Multiscale Deep Features
Visual saliency is a fundamental problem in both cognitive and computational
sciences, including computer vision. In this CVPR 2015 paper, we discover that
a high-quality visual saliency model can be trained with multiscale features
extracted using a popular deep learning architecture, convolutional neural
networks (CNNs), which have had many successes in visual recognition tasks. For
learning such saliency models, we introduce a neural network architecture,
which has fully connected layers on top of CNNs responsible for extracting
features at three different scales. We then propose a refinement method to
enhance the spatial coherence of our saliency results. Finally, aggregating
multiple saliency maps computed for different levels of image segmentation can
further boost the performance, yielding saliency maps better than those
generated from a single segmentation. To promote further research and
evaluation of visual saliency models, we also construct a new large database of
4447 challenging images and their pixelwise saliency annotation. Experimental
results demonstrate that our proposed method is capable of achieving
state-of-the-art performance on all public benchmarks, improving the F-Measure
by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset
(HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively
on these two datasets.Comment: To appear in CVPR 201
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection problem has been intensely studied for decades with various
commercial applications, it still meets problems in some real-world scenarios
due to numerous challenges, e.g. heavy facial occlusions, extremely low
resolutions, strong illumination, exceptionally pose variations, image or video
compression artifacts, etc. In this paper, we present a face detection approach
named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN)
to robustly solve the problems mentioned above. Similar to the region-based
CNNs, our proposed network consists of the region proposal component and the
region-of-interest (RoI) detection component. However, far apart of that
network, there are two main contributions in our proposed network that play a
significant role to achieve the state-of-the-art performance in face detection.
Firstly, the multi-scale information is grouped both in region proposal and RoI
detection to deal with tiny face regions. Secondly, our proposed network allows
explicit body contextual reasoning in the network inspired from the intuition
of human vision system. The proposed approach is benchmarked on two recent
challenging face detection databases, i.e. the WIDER FACE Dataset which
contains high degree of variability, as well as the Face Detection Dataset and
Benchmark (FDDB). The experimental results show that our proposed approach
trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE
Dataset by a large margin, and consistently achieves competitive results on
FDDB against the recent state-of-the-art face detection methods
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