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About this paper
Sheng Zhou, Junbin Xiao, Xun Yang, Peipei Song, Dan Guo, Angela Yao, Meng Wang, Tat-Seng Chua
Researchers from Hefei University of Technology, National University of Singapore, and University of Science and Technology of China introduce Grounded TextVideoQA, a new task requiring models to both answer questions and spatio-temporally localize relevant scene text in videos. Their T2S-QA model outperforms baselines in this weakly-supervised setting, while their analysis reveals scene-text detection and recognition as primary performance bottlenecks.
Key insights
- Explicitly requiring spatio-temporal grounding provides interpretability for TextVideoQA models and enables clearer diagnosis of errors, distinguishing between reasoning failures and OCR limitations.
- Evaluating grounded scene-text regions as "visual answers" offers a more flexible and robust assessment of model comprehension compared to stringent string matching for textual answers.
- Current scene-text detection and recognition systems, particularly in dynamic video environments, represent a significant bottleneck for achieving high performance in TextVideoQA, even more so than the higher-level reasoning component.
Summary by alphaXiv.
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