Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance.
We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals.
In this work, we aim to move beyond such limitations and propose a new framework to leverage context for person recognition.
Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions.
Ranked #45 on Person Re-Identification on DukeMTMC-reID
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest.