10,172 research outputs found
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
Most of the traditional convolutional neural networks (CNNs) implements
bottom-up approach (feed-forward) for image classifications. However, many
scientific studies demonstrate that visual perception in primates rely on both
bottom-up and top-down connections. Therefore, in this work, we propose a CNN
network with feedback structure for Solar power plant detection on
middle-resolution satellite images. To express the strength of the top-down
connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model
used for solar power plant classification on multi-spectral satellite data.
Moreover, we introduce a method to improve class activation mapping (CAM) to
our FB-Net, which takes advantage of multi-channel pulse coupled neural network
(m-PCNN) for weakly-supervised localization of the solar power plants from the
features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,
experimental results demonstrated promising results on both solar-power plant
image classification and detection task.Comment: 9 pages, 9 figures, 4 table
Priming Neural Networks
Visual priming is known to affect the human visual system to allow detection
of scene elements, even those that may have been near unnoticeable before, such
as the presence of camouflaged animals. This process has been shown to be an
effect of top-down signaling in the visual system triggered by the said cue. In
this paper, we propose a mechanism to mimic the process of priming in the
context of object detection and segmentation. We view priming as having a
modulatory, cue dependent effect on layers of features within a network. Our
results show how such a process can be complementary to, and at times more
effective than simple post-processing applied to the output of the network,
notably so in cases where the object is hard to detect such as in severe noise.
Moreover, we find the effects of priming are sometimes stronger when early
visual layers are affected. Overall, our experiments confirm that top-down
signals can go a long way in improving object detection and segmentation.Comment: fixed error in author nam
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