4,301 research outputs found
See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG
The Histogram of Oriented Gradient (HOG) descriptor has led to many advances
in computer vision over the last decade and is still part of many state of the
art approaches. We realize that the associated feature computation is piecewise
differentiable and therefore many pipelines which build on HOG can be made
differentiable. This lends to advanced introspection as well as opportunities
for end-to-end optimization. We present our implementation of HOG based
on the auto-differentiation toolbox Chumpy and show applications to pre-image
visualization and pose estimation which extends the existing differentiable
renderer OpenDR pipeline. Both applications improve on the respective
state-of-the-art HOG approaches
Spectral Analysis for Semantic Segmentation with Applications on Feature Truncation and Weak Annotation
We propose spectral analysis to investigate the correlation between the
accuracy and the resolution of segmentation maps for semantic segmentation. The
current networks predict segmentation maps on the down-sampled grid of images
to alleviate the computational cost. Moreover, these networks can be trained by
weak annotations that utilize only the coarse contour of segmentation maps.
Despite the successful achievement of these works utilizing the low-frequency
information of segmentation maps, however, the accuracy of resultant
segmentation maps may also be degraded in the regions near object boundaries.
It is yet unclear for a theoretical guideline to determine an optimal
down-sampled grid to strike the balance between the cost and the accuracy of
segmentation. We analyze the objective function (cross-entropy) and network
back-propagation process in frequency domain. We discover that cross-entropy
and key features of CNN are mainly contributed by the low-frequency components
of segmentation maps. This further provides us quantitative results to
determine the efficacy of down-sampled grid of segmentation maps. The analysis
is then validated on the two applications: the feature truncation method and
the block-wise annotation that limit the high-frequency components of the CNN
features and annotation, respectively. The results agree with our analysis.
Thus the success of the existing work utilizing low-frequency information of
segmentation maps now has theoretical foundation.Comment: 21 page
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