Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912138102046720 |
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| author | Heyward, Joseph Carreira, João Damen, Dima Zisserman, Andrew Pătrăucean, Viorica |
| author_facet | Heyward, Joseph Carreira, João Damen, Dima Zisserman, Andrew Pătrăucean, Viorica |
| contents | Following the successful 2023 edition, we organised the Second Perception Test challenge as a half-day workshop alongside the IEEE/CVF European Conference on Computer Vision (ECCV) 2024, with the goal of benchmarking state-of-the-art video models and measuring the progress since last year using the Perception Test benchmark. This year, the challenge had seven tracks (up from six last year) and covered low-level and high-level tasks, with language and non-language interfaces, across video, audio, and text modalities; the additional track covered hour-long video understanding and introduced a novel video QA benchmark 1h-walk VQA. Overall, the tasks in the different tracks were: object tracking, point tracking, temporal action localisation, temporal sound localisation, multiple-choice video question-answering, grounded video question-answering, and hour-long video question-answering. We summarise in this report the challenge tasks and results, and introduce in detail the novel hour-long video QA benchmark 1h-walk VQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19941 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark Heyward, Joseph Carreira, João Damen, Dima Zisserman, Andrew Pătrăucean, Viorica Computer Vision and Pattern Recognition Computation and Language Machine Learning Following the successful 2023 edition, we organised the Second Perception Test challenge as a half-day workshop alongside the IEEE/CVF European Conference on Computer Vision (ECCV) 2024, with the goal of benchmarking state-of-the-art video models and measuring the progress since last year using the Perception Test benchmark. This year, the challenge had seven tracks (up from six last year) and covered low-level and high-level tasks, with language and non-language interfaces, across video, audio, and text modalities; the additional track covered hour-long video understanding and introduced a novel video QA benchmark 1h-walk VQA. Overall, the tasks in the different tracks were: object tracking, point tracking, temporal action localisation, temporal sound localisation, multiple-choice video question-answering, grounded video question-answering, and hour-long video question-answering. We summarise in this report the challenge tasks and results, and introduce in detail the novel hour-long video QA benchmark 1h-walk VQA. |
| title | Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark |
| topic | Computer Vision and Pattern Recognition Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2411.19941 |