HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929281466105856 |
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| author | Zhang, Ce Stepputtis, Simon Campbell, Joseph Sycara, Katia Xie, Yaqi |
| author_facet | Zhang, Ce Stepputtis, Simon Campbell, Joseph Sycara, Katia Xie, Yaqi |
| contents | Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (SGG); however, many existing approaches assume undisturbed vision, i.e., the absence of real-world corruptions such as fog, snow, smoke, as well as non-uniform perturbations like sun glare or water drops. In this work, we propose a novel SGG benchmark containing procedurally generated weather corruptions and other transformations over the Visual Genome dataset. Further, we introduce a corresponding approach, Hierarchical Knowledge Enhanced Robust Scene Graph Generation (HiKER-SGG), providing a strong baseline for scene graph generation under such challenging setting. At its core, HiKER-SGG utilizes a hierarchical knowledge graph in order to refine its predictions from coarse initial estimates to detailed predictions. In our extensive experiments, we show that HiKER-SGG does not only demonstrate superior performance on corrupted images in a zero-shot manner, but also outperforms current state-of-the-art methods on uncorrupted SGG tasks. Code is available at https://github.com/zhangce01/HiKER-SGG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12033 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation Zhang, Ce Stepputtis, Simon Campbell, Joseph Sycara, Katia Xie, Yaqi Computer Vision and Pattern Recognition Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (SGG); however, many existing approaches assume undisturbed vision, i.e., the absence of real-world corruptions such as fog, snow, smoke, as well as non-uniform perturbations like sun glare or water drops. In this work, we propose a novel SGG benchmark containing procedurally generated weather corruptions and other transformations over the Visual Genome dataset. Further, we introduce a corresponding approach, Hierarchical Knowledge Enhanced Robust Scene Graph Generation (HiKER-SGG), providing a strong baseline for scene graph generation under such challenging setting. At its core, HiKER-SGG utilizes a hierarchical knowledge graph in order to refine its predictions from coarse initial estimates to detailed predictions. In our extensive experiments, we show that HiKER-SGG does not only demonstrate superior performance on corrupted images in a zero-shot manner, but also outperforms current state-of-the-art methods on uncorrupted SGG tasks. Code is available at https://github.com/zhangce01/HiKER-SGG. |
| title | HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12033 |