  To achieve robustness in Re-Identification, standard methods leverage
tracking information in a Video-To-Video fashion. However, these solutions face
a large drop in performance for single image queries (e.g., Image-To-Video
setting). Recent works address this severe degradation by transferring temporal
information from a Video-based network to an Image-based one. In this work, we
devise a training strategy that allows the transfer of a superior knowledge,
arising from a set of views depicting the target object. Our proposal - Views
Knowledge Distillation (VKD) - pins this visual variety as a supervision signal
within a teacher-student framework, where the teacher educates a student who
observes fewer views. As a result, the student outperforms not only its teacher
but also the current state-of-the-art in Image-To-Video by a wide margin (6.3%
mAP on MARS, 8.6% on Duke-Video-ReId and 5% on VeRi-776). A thorough analysis -
on Person, Vehicle and Animal Re-ID - investigates the properties of VKD from a
qualitatively and quantitatively perspective. Code is available at
https://github.com/aimagelab/VKD.
