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Vision-Language Models (VLMs) are expected to be capable of reasoning withcommonsense knowledge as human beings. One example is that humans can reasonwhere and when an image is taken based on their knowledge. This makes us wonderif, based on visual cues, Vision-Language Models that are pre-trained withlarge-scale image-text resources can achieve and even outperform human'scapability in reasoning times and location. To address this question, wepropose a two-stage \recognition\space and \reasoning\space probing task,applied to discriminative and generative VLMs to uncover whether VLMs canrecognize times and location-relevant features and further reason about it. Tofacilitate the investigation, we introduce WikiTiLo, a well-curated imagedataset compromising images with rich socio-cultural cues. In the extensiveexperimental studies, we find that although VLMs can effectively retainrelevant features in visual encoders, they still fail to make perfectreasoning. We will release our dataset and codes to facilitate future studies.

Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning