  The gap in representations between image and video makes Image-to-Video
Re-identification (I2V Re-ID) challenging, and recent works formulate this
problem as a knowledge distillation (KD) process. In this paper, we propose a
mutual discriminative knowledge distillation framework to transfer a
video-based richer representation to an image based representation more
effectively. Specifically, we propose the triplet contrast loss (TCL), a novel
loss designed for KD. During the KD process, the TCL loss transfers the local
structure, exploits the higher order information, and mitigates the
misalignment of the heterogeneous output of teacher and student networks.
Compared with other losses for KD, the proposed TCL loss selectively transfers
the local discriminative features from teacher to student, making it effective
in the ReID. Besides the TCL loss, we adopt mutual learning to regularize both
the teacher and student networks training. Extensive experiments demonstrate
the effectiveness of our method on the MARS, DukeMTMC-VideoReID and VeRi-776
benchmarks.
