5,456 research outputs found
Object detection via a multi-region & semantic segmentation-aware CNN model
We propose an object detection system that relies on a multi-region deep
convolutional neural network (CNN) that also encodes semantic
segmentation-aware features. The resulting CNN-based representation aims at
capturing a diverse set of discriminative appearance factors and exhibits
localization sensitivity that is essential for accurate object localization. We
exploit the above properties of our recognition module by integrating it on an
iterative localization mechanism that alternates between scoring a box proposal
and refining its location with a deep CNN regression model. Thanks to the
efficient use of our modules, we detect objects with very high localization
accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we
achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published
work by a significant margin.Comment: Extended technical report -- short version to appear at ICCV 201
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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