Beyond Part Models: Person Retrieval
with Refined Part Pooling
(and A Strong Convolutional Baseline)
Abstract. Employing part-level features offers fine-grained information
for pedestrian image description.A prerequisite of part discovery is that
each part should be well located. Instead of using external resources
like pose estimator, we consider content consistency within each part for
precise part location. Specifically, we target at learning discriminative
part-informed features for person retrieval and make two contributions.
(i) A network named Part-based Convolutional Baseline (PCB). Given
an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves
competitive results with the state-of-the-art methods, proving itself as
a strong convolutional baseline for person retrieval. (ii) A refined part
pooling (RPP) method. Uniform partition inevitably incurs outliers in
each part, which are in fact more similar to other parts. RPP re-assigns
these outliers to the parts they are closest to, resulting in refined parts
with enhanced within-part consistency. Experiment confirms that RPP
allows PCB to gain another round of performance boost. For instance, on
the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)%
rank-1 accuracy, surpassing the state of the art by a large margin. Code
is available at: https://github.com/syfafterzy/PCB_RPP