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

Abstract

This paper focuses on reasoning about the location and time behind images. Given that pre-trained vision-language models (VLMs) exhibit excellent image and text understanding capabilities, most existing methods leverage them to match visual cues with location and time-related descriptions. However, these methods cannot look beyond the actual content of an image, failing to produce satisfactory reasoning results, as such reasoning requires connecting visual details with rich external cues (e.g., relevant event contexts). To this end, we propose a novel reasoning method, QR-CLIP , aim at enhancing the model's ability to reason about location and time through interaction with external explicit knowledge such as Wikipedia. Specifically, QR-CLIP consists of two modules: 1) The Quantity module abstracts the image into multiple distinct representations and uses them to search and gather external knowledge from different perspectives that are beneficial to model reasoning. 2) The Relevance module filters the visual features and the searched explicit knowledge and dynamically integrates them to form a comprehensive reasoning result. Extensive experiments demonstrate the effectiveness and generalizability of QR-CLIP. On the WikiTiLo dataset, QR-CLIP boosts the accuracy of location (country) and time reasoning by 7.03% and 2.22%, respectively, over previous SOTA methods. On the more challenging TARA dataset, it improves the accuracy for location and time reasoning by 3.05% and 2.45%, respectively. The source code is at https://github.com/Shi-Wm/QR-CLIP .