The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916309205254144 |
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| author | Zhang, Hailiang Chao, Dian Guan, Zhihao Yang, Yang |
| author_facet | Zhang, Hailiang Chao, Dian Guan, Zhihao Yang, Yang |
| contents | In this paper, we introduce a grounded video question-answering solution. Our research reveals that the fixed official baseline method for video question answering involves two main steps: visual grounding and object tracking. However, a significant challenge emerges during the initial step, where selected frames may lack clearly identifiable target objects. Furthermore, single images cannot address questions like "Track the container from which the person pours the first time." To tackle this issue, we propose an alternative two-stage approach:(1) First, we leverage the VALOR model to answer questions based on video information.(2) concatenate the answered questions with their respective answers. Finally, we employ TubeDETR to generate bounding boxes for the targets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01907 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA Zhang, Hailiang Chao, Dian Guan, Zhihao Yang, Yang Computer Vision and Pattern Recognition Machine Learning In this paper, we introduce a grounded video question-answering solution. Our research reveals that the fixed official baseline method for video question answering involves two main steps: visual grounding and object tracking. However, a significant challenge emerges during the initial step, where selected frames may lack clearly identifiable target objects. Furthermore, single images cannot address questions like "Track the container from which the person pours the first time." To tackle this issue, we propose an alternative two-stage approach:(1) First, we leverage the VALOR model to answer questions based on video information.(2) concatenate the answered questions with their respective answers. Finally, we employ TubeDETR to generate bounding boxes for the targets. |
| title | The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2407.01907 |