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--- groupmeeting-fall2014 [2014/12/04 12:29]
shallman [Week 9 - Dec 4th - Sam - DBH4011]
+++ groupmeeting-fall2014 [2014/12/10 12:08] (current)
jsupanci [Week 11 - Dec 18th - James - DBH4011]
@@ Line 67: / Line 67: @@
 [[http://vision.ucsd.edu/~pdollar/files/papers/NamNIPS14ldcf.pdf|vision.ucsd.edu/~pdollar/files/papers/NamNIPS14ldcf.pdf]]
-==== Week 10 - Dec 11th - Greg - DBH4011 ====
+==== Week 10 - Dec 11th - James - DBH4011 ====
-Paper:
+Paper: Filter Forests for learning Data-Dependent Convolutional Kernels
-==== Week 11 - Dec 18th - James - DBH4011 ====
+Abstract: We propose ‘filter forests’ (FF), an efficient new discrimi-\\
+native approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filter- ing, where it attempts to learn the optimal filtering kernels to be applied to each data point. The model can learn both the size of the kernel and its values, conditioned on the ob- servation and its spatial or temporal context. We show that FF compares favorably to both Markov random field based and recently proposed regression forest based approaches for labeling problems in terms of efficiency and accuracy. In particular, we demonstrate how FF can be used to learn optimal denoising filters for natural images as well as for other tasks such as depth image refinement, and 1D signal magnitude estimation. Numerous experiments and quanti- tative comparisons show that FFs achieve accuracy at par or superior to recent state of the art techniques, while being several orders of magnitude faster
+http://research.microsoft.com/pubs/217099/CVPR2014ForestFiltering.pdf
+==== Week 11 - Dec 18th - Greg - DBH4011 ====
 Paper:

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