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arXiv · 2501.14210

PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction

Hammad Ayyubi·Xuande Feng·Junzhang Liu·Xudong Lin·Zhecan Wang·Shih-Fu Chang
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Abstract

The task of predicting time and location from images is challenging and requires complex human-like puzzle-solving ability over different clues. In this work, we formalize this ability into core skills and implement them using different modules in an expert pipeline called PuzzleGPT. PuzzleGPT consists of a perceiver to identify visual clues, a reasoner to deduce prediction candidates, a combiner to combinatorially combine information from different clues, a web retriever to get external knowledge if the task can't be solved locally, and a noise filter for robustness. This results in a zero-shot, interpretable, and robust approach that records state-of-the-art performance on two datasets -- TARA and WikiTilo. PuzzleGPT outperforms large VLMs such as BLIP-2, InstructBLIP, LLaVA, and even GPT-4V, as well as automatically generated reasoning pipelines like VisProg, by at least 32% and 38%, respectively. It even rivals or surpasses finetuned models.

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Hammad Ayyubi, Xuande Feng, Junzhang Liu, Xudong Lin, Zhecan Wang, Shih-Fu Chang. 2025-01-24. PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction. https://arxiv.org/abs/2501.14210

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