QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning

TL;DR

QR-CLIP is a novel model that leverages open-world knowledge to improve location and time reasoning from images, outperforming previous methods significantly.

Contribution

The paper introduces QR-CLIP, a new model inspired by Horn's QR theory, integrating open-world knowledge for enhanced location and time inference from images.

Findings

Outperforms previous SOTA by about 10% in location reasoning

Achieves approximately 130% relative improvement in time reasoning

Establishes a technical foundation for open-world knowledge integration in reasoning tasks

Abstract

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.