258 research outputs found
Convolutional Feature Masking for Joint Object and Stuff Segmentation
The topic of semantic segmentation has witnessed considerable progress due to
the powerful features learned by convolutional neural networks (CNNs). The
current leading approaches for semantic segmentation exploit shape information
by extracting CNN features from masked image regions. This strategy introduces
artificial boundaries on the images and may impact the quality of the extracted
features. Besides, the operations on the raw image domain require to compute
thousands of networks on a single image, which is time-consuming. In this
paper, we propose to exploit shape information via masking convolutional
features. The proposal segments (e.g., super-pixels) are treated as masks on
the convolutional feature maps. The CNN features of segments are directly
masked out from these maps and used to train classifiers for recognition. We
further propose a joint method to handle objects and "stuff" (e.g., grass, sky,
water) in the same framework. State-of-the-art results are demonstrated on
benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling
computational speed.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
201
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
State-of-the-art object detection networks depend on region proposal
algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN
have reduced the running time of these detection networks, exposing region
proposal computation as a bottleneck. In this work, we introduce a Region
Proposal Network (RPN) that shares full-image convolutional features with the
detection network, thus enabling nearly cost-free region proposals. An RPN is a
fully convolutional network that simultaneously predicts object bounds and
objectness scores at each position. The RPN is trained end-to-end to generate
high-quality region proposals, which are used by Fast R-CNN for detection. We
further merge RPN and Fast R-CNN into a single network by sharing their
convolutional features---using the recently popular terminology of neural
networks with 'attention' mechanisms, the RPN component tells the unified
network where to look. For the very deep VGG-16 model, our detection system has
a frame rate of 5fps (including all steps) on a GPU, while achieving
state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS
COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015
competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning
entries in several tracks. Code has been made publicly available.Comment: Extended tech repor
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