16,494 research outputs found
HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
We propose a two-stage convolutional neural network (CNN) architecture for
robust recognition of hand gestures, called HGR-Net, where the first stage
performs accurate semantic segmentation to determine hand regions, and the
second stage identifies the gesture. The segmentation stage architecture is
based on the combination of fully convolutional residual network and atrous
spatial pyramid pooling. Although the segmentation sub-network is trained
without depth information, it is particularly robust against challenges such as
illumination variations and complex backgrounds. The recognition stage deploys
a two-stream CNN, which fuses the information from the red-green-blue and
segmented images by combining their deep representations in a fully connected
layer before classification. Extensive experiments on public datasets show that
our architecture achieves almost as good as state-of-the-art performance in
segmentation and recognition of static hand gestures, at a fraction of training
time, run time, and model size. Our method can operate at an average of 23 ms
per frame
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Existing deep convolutional neural networks (CNNs) require a fixed-size
(e.g., 224x224) input image. This requirement is "artificial" and may reduce
the recognition accuracy for the images or sub-images of an arbitrary
size/scale. In this work, we equip the networks with another pooling strategy,
"spatial pyramid pooling", to eliminate the above requirement. The new network
structure, called SPP-net, can generate a fixed-length representation
regardless of image size/scale. Pyramid pooling is also robust to object
deformations. With these advantages, SPP-net should in general improve all
CNN-based image classification methods. On the ImageNet 2012 dataset, we
demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures
despite their different designs. On the Pascal VOC 2007 and Caltech101
datasets, SPP-net achieves state-of-the-art classification results using a
single full-image representation and no fine-tuning.
The power of SPP-net is also significant in object detection. Using SPP-net,
we compute the feature maps from the entire image only once, and then pool
features in arbitrary regions (sub-images) to generate fixed-length
representations for training the detectors. This method avoids repeatedly
computing the convolutional features. In processing test images, our method is
24-102x faster than the R-CNN method, while achieving better or comparable
accuracy on Pascal VOC 2007.
In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our
methods rank #2 in object detection and #3 in image classification among all 38
teams. This manuscript also introduces the improvement made for this
competition.Comment: This manuscript is the accepted version for IEEE Transactions on
Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo
Deep Pyramidal Residual Networks
Deep convolutional neural networks (DCNNs) have shown remarkable performance
in image classification tasks in recent years. Generally, deep neural network
architectures are stacks consisting of a large number of convolutional layers,
and they perform downsampling along the spatial dimension via pooling to reduce
memory usage. Concurrently, the feature map dimension (i.e., the number of
channels) is sharply increased at downsampling locations, which is essential to
ensure effective performance because it increases the diversity of high-level
attributes. This also applies to residual networks and is very closely related
to their performance. In this research, instead of sharply increasing the
feature map dimension at units that perform downsampling, we gradually increase
the feature map dimension at all units to involve as many locations as
possible. This design, which is discussed in depth together with our new
insights, has proven to be an effective means of improving generalization
ability. Furthermore, we propose a novel residual unit capable of further
improving the classification accuracy with our new network architecture.
Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown
that our network architecture has superior generalization ability compared to
the original residual networks. Code is available at
https://github.com/jhkim89/PyramidNet}Comment: Accepted to CVPR 201
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