Spectrum-guided Feature Enhancement Network for Event Person
  Re-Identification
Hongchen Tan Yi Zhang Xiuping Liu Baocai Yin Nan Ma Xin Li Huchuan Lu
As a cutting-edge biosensor, the event camera holds significant potential in
the field of computer vision, particularly regarding privacy preservation.
However, compared to traditional cameras, event streams often contain noise and
possess extremely sparse semantics, posing a formidable challenge for
event-based person re-identification (event Re-ID). To address this, we
introduce a novel event person re-identification network: the Spectrum-guided
Feature Enhancement Network (SFE-Net). This network consists of two innovative
components: the Multi-grain Spectrum Attention Mechanism (MSAM) and the
Consecutive Patch Dropout Module (CPDM). MSAM employs a fourier spectrum
transform strategy to filter event noise, while also utilizing an event-guided
multi-granularity attention strategy to enhance and capture discriminative
person semantics. CPDM employs a consecutive patch dropout strategy to generate
multiple incomplete feature maps, encouraging the deep Re-ID model to equally
perceive each effective region of the person's body and capture robust person
descriptors. Extensive experiments on Event Re-ID datasets demonstrate that our
SFE-Net achieves the best performance in this task.