NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866911779847667712 |
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| author | Qian, Tianwen Chen, Jingjing Zhuo, Linhai Jiao, Yang Jiang, Yu-Gang |
| author_facet | Qian, Tianwen Chen, Jingjing Zhuo, Linhai Jiao, Yang Jiang, Yu-Gang |
| contents | We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated programmatically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://github.com/qiantianwen/NuScenes-QA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_14836 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario Qian, Tianwen Chen, Jingjing Zhuo, Linhai Jiao, Yang Jiang, Yu-Gang Computer Vision and Pattern Recognition We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated programmatically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://github.com/qiantianwen/NuScenes-QA. |
| title | NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2305.14836 |