  Person re-identification (re-ID) requires densely distributed cameras. In
practice, the person of interest may not be captured by cameras and, therefore,
needs to be retrieved using subjective information (e.g., sketches from
witnesses). Previous research defines this case using the sketch as sketch
re-identification (Sketch re-ID) and focuses on eliminating the domain gap.
Actually, subjectivity is another significant challenge. We model and
investigate it by posing a new dataset with multi-witness descriptions. It
features two aspects. 1) Large-scale. It contains over 4,763 sketches and
32,668 photos, making it the largest Sketch re-ID dataset. 2) Multi-perspective
and multi-style. Our dataset offers multiple sketches for each identity.
Witnesses' subjective cognition provides multiple perspectives on the same
individual, while different artists' drawing styles provide variation in sketch
styles. We further have two novel designs to alleviate the challenge of
subjectivity. 1) Fusing subjectivity. We propose a non-local (NL) fusion module
that gathers sketches from different witnesses for the same identity. 2)
Introducing objectivity. An AttrAlign module utilizes attributes as an implicit
mask to align cross-domain features. To push forward the advance of Sketch
re-ID, we set three benchmarks (large-scale, multi-style, cross-style).
Extensive experiments demonstrate our leading performance in these benchmarks.
Dataset and Codes are publicly available at:
https://github.com/Lin-Kayla/subjectivity-sketch-reid
