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The \emph{receptive fields} of deep learning classification models determinethe regions of the input data that have the most significance for providingcorrect decisions. The primary way to learn such receptive fields is to trainthe models upon masked data, which helps the networks to ignore any unwantedregions, but has two major drawbacks: 1) it often yields edge-sensitivedecision processes; and 2) augments the computational cost of the inferencephase considerably. This paper describes a solution for implicitly driving theinference of the networks' receptive fields, by creating synthetic learningdata composed of interchanged segments that should be \emph{apriori}important/irrelevant for the network decision. In practice, we use asegmentation module to distinguish between the foreground(important)/background (irrelevant) parts of each learning instance, andrandomly swap segments between image pairs, while keeping the class labelexclusively consistent with the label of the deemed important segments. Thisstrategy typically drives the networks to early convergence and appropriatesolutions, where the identity and clutter descriptions are not correlated.Moreover, this data augmentation solution has various interesting properties:1) it is parameter-free; 2) it fully preserves the label information; and, 3)it is compatible with the typical data augmentation techniques. In theempirical validation, we considered the person re-identification problem andevaluated the effectiveness of the proposed solution in the well-known\emph{Richly Annotated Pedestrian} (RAP) dataset for two different settings(\emph{upper-body} and \emph{full-body}), observing highly competitive resultsover the state-of-the-art. Under a reproducible research paradigm, both thecode and the empirical evaluation protocol are available at\url{https://github.com/Ehsan-Yaghoubi/reid-strong-baseline}.

Person Re-identification: Implicitly Defining the Receptive Fields of Deep Learning Classification Frameworks