46,171 research outputs found
Recurrent Segmentation for Variable Computational Budgets
State-of-the-art systems for semantic image segmentation use feed-forward
pipelines with fixed computational costs. Building an image segmentation system
that works across a range of computational budgets is challenging and
time-intensive as new architectures must be designed and trained for every
computational setting. To address this problem we develop a recurrent neural
network that successively improves prediction quality with each iteration.
Importantly, the RNN may be deployed across a range of computational budgets by
merely running the model for a variable number of iterations. We find that this
architecture is uniquely suited for efficiently segmenting videos. By
exploiting the segmentation of past frames, the RNN can perform video
segmentation at similar quality but reduced computational cost compared to
state-of-the-art image segmentation methods. When applied to static images in
the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a
speed-accuracy curve that saturates near the performance of state-of-the-art
segmentation methods
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
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