  Daily images may convey abstract meanings that require us to memorize and
infer profound information from them. To encourage such human-like reasoning,
in this work, we teach machines to predict where and when it was taken rather
than performing basic tasks like traditional segmentation or classification.
Inspired by Horn's QR theory, we designed a novel QR-CLIP model consisting of
two components: 1) the Quantity module first retrospects more open-world
knowledge as the candidate language inputs; 2) the Relevance module carefully
estimates vision and language cues and infers the location and time.
Experiments show our QR-CLIP's effectiveness, and it outperforms the previous
SOTA on each task by an average of about 10% and 130% relative lift in terms of
location and time reasoning. This study lays a technical foundation for
location and time reasoning and suggests that effectively introducing
open-world knowledge is one of the panaceas for the tasks.
