Abstract
Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details.
However, prevailing person re-identification(re-ID) methods 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 expensive 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-ofthe-art results on all five datasets that we evaluate on.
We then propose two new formulations of the person reID 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