  Pedestrian detection is a crucial field of computer vision research which can
be adopted in various real-world applications (e.g., self-driving systems).
However, despite noticeable evolution of pedestrian detection, pedestrian
representations learned within a detection framework are usually limited to
particular scene data in which they were trained. Therefore, in this paper, we
propose a novel approach to construct versatile pedestrian knowledge bank
containing representative pedestrian knowledge which can be applicable to
various detection frameworks and adopted in diverse scenes. We extract
generalized pedestrian knowledge from a large-scale pretrained model, and we
curate them by quantizing most representative features and guiding them to be
distinguishable from background scenes. Finally, we construct versatile
pedestrian knowledge bank which is composed of such representations, and then
we leverage it to complement and enhance pedestrian features within a
pedestrian detection framework. Through comprehensive experiments, we validate
the effectiveness of our method, demonstrating its versatility and
outperforming state-of-the-art detection performances.
