2,733 research outputs found
Energy-efficient Amortized Inference with Cascaded Deep Classifiers
Deep neural networks have been remarkable successful in various AI tasks but
often cast high computation and energy cost for energy-constrained applications
such as mobile sensing. We address this problem by proposing a novel framework
that optimizes the prediction accuracy and energy cost simultaneously, thus
enabling effective cost-accuracy trade-off at test time. In our framework, each
data instance is pushed into a cascade of deep neural networks with increasing
sizes, and a selection module is used to sequentially determine when a
sufficiently accurate classifier can be used for this data instance. The
cascade of neural networks and the selection module are jointly trained in an
end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between
the computational cost and the predictive accuracy. Our method is able to
simultaneously improve the accuracy and efficiency by learning to assign easy
instances to fast yet sufficiently accurate classifiers to save computation and
energy cost, while assigning harder instances to deeper and more powerful
classifiers to ensure satisfiable accuracy. With extensive experiments on
several image classification datasets using cascaded ResNet classifiers, we
demonstrate that our method outperforms the standard well-trained ResNets in
accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100
datasets and 66% on the ImageNet dataset, respectively
Asymmetric Totally-corrective Boosting for Real-time Object Detection
Real-time object detection is one of the core problems in computer vision.
The cascade boosting framework proposed by Viola and Jones has become the
standard for this problem. In this framework, the learning goal for each node
is asymmetric, which is required to achieve a high detection rate and a
moderate false positive rate. We develop new boosting algorithms to address
this asymmetric learning problem. We show that our methods explicitly optimize
asymmetric loss objectives in a totally corrective fashion. The methods are
totally corrective in the sense that the coefficients of all selected weak
classifiers are updated at each iteration. In contract, conventional boosting
like AdaBoost is stage-wise in that only the current weak classifier's
coefficient is updated. At the heart of the totally corrective boosting is the
column generation technique. Experiments on face detection show that our
methods outperform the state-of-the-art asymmetric boosting methods.Comment: 14 pages, published in Asian Conf. Computer Vision 201
Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework
Background-Foreground classification is a well-studied problem in computer
vision. Due to the pixel-wise nature of modeling and processing in the
algorithm, it is usually difficult to satisfy real-time constraints. There is a
trade-off between the speed (because of model complexity) and accuracy.
Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We
achieve a good improvement in speed without compromising the accuracy with
respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and
17 percent average improvement in accuracy over Wallflowers surveillance
datasets. The CoG is then demonstrated to over the latent space representation
of images of a convolutional variational autoencoder(VAE). We provide initial
results over CDW-2014 dataset, which could speed up background subtraction for
deep architectures.Comment: Accepted for National Conference on Computer Vision, Pattern
Recognition, Image Processing and Graphics (NCVPRIPG 2019
Asymmetric Pruning for Learning Cascade Detectors
Cascade classifiers are one of the most important contributions to real-time
object detection. Nonetheless, there are many challenging problems arising in
training cascade detectors. One common issue is that the node classifier is
trained with a symmetric classifier. Having a low misclassification error rate
does not guarantee an optimal node learning goal in cascade classifiers, i.e.,
an extremely high detection rate with a moderate false positive rate. In this
work, we present a new approach to train an effective node classifier in a
cascade detector. The algorithm is based on two key observations: 1) Redundant
weak classifiers can be safely discarded; 2) The final detector should satisfy
the asymmetric learning objective of the cascade architecture. To achieve this,
we separate the classifier training into two steps: finding a pool of
discriminative weak classifiers/features and training the final classifier by
pruning weak classifiers which contribute little to the asymmetric learning
criterion (asymmetric classifier construction). Our model reduction approach
helps accelerate the learning time while achieving the pre-determined learning
objective. Experimental results on both face and car data sets verify the
effectiveness of the proposed algorithm. On the FDDB face data sets, our
approach achieves the state-of-the-art performance, which demonstrates the
advantage of our approach.Comment: 14 page
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