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

• URL: http://arxiv.org/abs/2501.14210v1
• Date: Fri, 24 Jan 2025 03:28:37 GMT
• Title: PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction
• Authors: Hammad Ayyubi, Xuande Feng, Junzhang Liu, Xudong Lin, Zhecan Wang, Shih-Fu Chang,
• Abstract summary: PuzzleGPT consists of a perceiver to identify visual clues, a reasoner to deduce prediction candidates, a web retriever to get external knowledge if the task can't be solved locally. This results in a zero-shot, interpretable, and robust approach that records state-of-the-art performance on two datasets.
• Score: 35.77541376401752
• License:
• 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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