28,156 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
Responsibility and blame: a structural-model approach
Causality is typically treated an all-or-nothing concept; either A is a cause
of B or it is not. We extend the definition of causality introduced by Halpern
and Pearl [2001] to take into account the degree of responsibility of A for B.
For example, if someone wins an election 11--0, then each person who votes for
him is less responsible for the victory than if he had won 6--5. We then define
a notion of degree of blame, which takes into account an agent's epistemic
state. Roughly speaking, the degree of blame of A for B is the expected degree
of responsibility of A for B, taken over the epistemic state of an agent
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