HCQA-1.5 @ Ego4D EgoSchema Challenge 2025
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910969776570368 |
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| author | Zhang, Haoyu Feng, Yisen Chu, Qiaohui Liu, Meng Guan, Weili Wang, Yaowei Nie, Liqiang |
| author_facet | Zhang, Haoyu Feng, Yisen Chu, Qiaohui Liu, Meng Guan, Weili Wang, Yaowei Nie, Liqiang |
| contents | In this report, we present the method that achieves third place for Ego4D EgoSchema Challenge in CVPR 2025. To improve the reliability of answer prediction in egocentric video question answering, we propose an effective extension to the previously proposed HCQA framework. Our approach introduces a multi-source aggregation strategy to generate diverse predictions, followed by a confidence-based filtering mechanism that selects high-confidence answers directly. For low-confidence cases, we incorporate a fine-grained reasoning module that performs additional visual and contextual analysis to refine the predictions. Evaluated on the EgoSchema blind test set, our method achieves 77% accuracy on over 5,000 human-curated multiple-choice questions, outperforming last year's winning solution and the majority of participating teams. Our code will be added at https://github.com/Hyu-Zhang/HCQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HCQA-1.5 @ Ego4D EgoSchema Challenge 2025 Zhang, Haoyu Feng, Yisen Chu, Qiaohui Liu, Meng Guan, Weili Wang, Yaowei Nie, Liqiang Computer Vision and Pattern Recognition Artificial Intelligence In this report, we present the method that achieves third place for Ego4D EgoSchema Challenge in CVPR 2025. To improve the reliability of answer prediction in egocentric video question answering, we propose an effective extension to the previously proposed HCQA framework. Our approach introduces a multi-source aggregation strategy to generate diverse predictions, followed by a confidence-based filtering mechanism that selects high-confidence answers directly. For low-confidence cases, we incorporate a fine-grained reasoning module that performs additional visual and contextual analysis to refine the predictions. Evaluated on the EgoSchema blind test set, our method achieves 77% accuracy on over 5,000 human-curated multiple-choice questions, outperforming last year's winning solution and the majority of participating teams. Our code will be added at https://github.com/Hyu-Zhang/HCQA. |
| title | HCQA-1.5 @ Ego4D EgoSchema Challenge 2025 |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.20644 |