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Title: Resource Aware Person Re-identification across Multiple Resolutions
Not all people are equally easy to identify: color statistics might be enough for some cases while others might re- quire careful reasoning about high- and low-level details. However, prevailing person re-identification(re-ID) meth- ods use one-size-fits-all high-level embeddings from deep convolutional networks for all cases. This might limit their accuracy on difficult examples or makes them needlessly ex- pensive for the easy ones. To remedy this, we present a new person re-ID model that combines effective embeddings built on multiple convolutional network layers, trained with deep-supervision. On traditional re-ID benchmarks, our method improves substantially over the previous state-of- the-art results on all five datasets that we evaluate on. We then propose two new formulations of the person re- ID problem under resource-constraints, and show how our model can be used to effectively trade off accuracy and computation in the presence of resource constraints.  more » « less
Award ID(s):
1724282
PAR ID:
10064657
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
CVPR 2018
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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