Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues

• URL: http://arxiv.org/abs/2412.12502v1
• Date: Tue, 17 Dec 2024 03:06:12 GMT
• Title: Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues
• Authors: Yan Zhang, Gangyan Zeng, Huawen Shen, Daiqing Wu, Yu Zhou, Can Ma,
• Abstract summary: Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly textual reasoning and visual information in a given video.<n>We propose the TEA (stands for textbfTrack thbfE bftextAlanguageer'') method that better extends the generative TextVQA framework from image to video.
• Score: 8.797350517975477
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly reasoning textual and visual information in a given video. Inspired by the development of TextVQA in image domain, existing Video TextVQA approaches leverage a language model (e.g. T5) to process text-rich multiple frames and generate answers auto-regressively. Nevertheless, the spatio-temporal relationships among visual entities (including scene text and objects) will be disrupted and models are susceptible to interference from unrelated information, resulting in irrational reasoning and inaccurate answering. To tackle these challenges, we propose the TEA (stands for ``\textbf{T}rack th\textbf{E} \textbf{A}nswer'') method that better extends the generative TextVQA framework from image to video. TEA recovers the spatio-temporal relationships in a complementary way and incorporates OCR-aware clues to enhance the quality of reasoning questions. Extensive experiments on several public Video TextVQA datasets validate the effectiveness and generalization of our framework. TEA outperforms existing TextVQA methods, video-language pretraining methods and video large language models by great margins.

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