VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915612052160512 |
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| author | Phukan, Arpan Pandey, Anupam Bodo, Deepjyoti Ekbal, Asif |
| author_facet | Phukan, Arpan Pandey, Anupam Bodo, Deepjyoti Ekbal, Asif |
| contents | Multi-hop Question Generation (QG) effectively evaluates reasoning but remains confined to text; Video Question Generation (VideoQG) is limited to zero-hop questions over single segments. To address this, we introduce VideoChain, a novel Multi-hop Video Question Generation (MVQG) framework designed to generate questions that require reasoning across multiple, temporally separated video segments. VideoChain features a modular architecture built on a modified BART backbone enhanced with video embeddings, capturing textual and visual dependencies. Using the TVQA+ dataset, we automatically construct the large-scale MVQ-60 dataset by merging zero-hop QA pairs, ensuring scalability and diversity. Evaluations show VideoChain's strong performance across standard generation metrics: ROUGE-L (0.6454), ROUGE-1 (0.6854), BLEU-1 (0.6711), BERTScore-F1 (0.7967), and semantic similarity (0.8110). These results highlight the model's ability to generate coherent, contextually grounded, and reasoning-intensive questions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_08348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation Phukan, Arpan Pandey, Anupam Bodo, Deepjyoti Ekbal, Asif Computer Vision and Pattern Recognition Multi-hop Question Generation (QG) effectively evaluates reasoning but remains confined to text; Video Question Generation (VideoQG) is limited to zero-hop questions over single segments. To address this, we introduce VideoChain, a novel Multi-hop Video Question Generation (MVQG) framework designed to generate questions that require reasoning across multiple, temporally separated video segments. VideoChain features a modular architecture built on a modified BART backbone enhanced with video embeddings, capturing textual and visual dependencies. Using the TVQA+ dataset, we automatically construct the large-scale MVQ-60 dataset by merging zero-hop QA pairs, ensuring scalability and diversity. Evaluations show VideoChain's strong performance across standard generation metrics: ROUGE-L (0.6454), ROUGE-1 (0.6854), BLEU-1 (0.6711), BERTScore-F1 (0.7967), and semantic similarity (0.8110). These results highlight the model's ability to generate coherent, contextually grounded, and reasoning-intensive questions. |
| title | VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.08348 |