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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.13218 |
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| _version_ | 1866918315341905920 |
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| author | Vozniak, Igor Mueller, Philipp Lipp, Nils Sprenger, Janis Poddubnyy, Konstantin Hovhannisyan, Davit Mueller, Christian Bulling, Andreas Slusallek, Philipp |
| author_facet | Vozniak, Igor Mueller, Philipp Lipp, Nils Sprenger, Janis Poddubnyy, Konstantin Hovhannisyan, Davit Mueller, Christian Bulling, Andreas Slusallek, Philipp |
| contents | The object-based nature of human visual attention is well-known in cognitive science, but has only played a minor role in computational visual attention models so far. This is mainly due to a lack of suitable datasets and evaluation metrics for object-based attention. To address these limitations, we present ObjectVisA-120 -- a novel 120-participant dataset of spatial street-crossing navigation in virtual reality specifically geared to object-based attention evaluations. The uniqueness of the presented dataset lies in the ethical and safety affiliated challenges that make collecting comparable data in real-world environments highly difficult. ObjectVisA-120 not only features accurate gaze data and a complete state-space representation of objects in the virtual environment, but it also offers variable scenario complexities and rich annotations, including panoptic segmentation, depth information, and vehicle keypoints. We further propose object-based similarity (oSIM) as a novel metric to evaluate the performance of object-based visual attention models, a previously unexplored performance characteristic. Our evaluations show that explicitly optimising for object-based attention not only improves oSIM performance but also leads to an improved model performance on common metrics. In addition, we present SUMGraph, a Mamba U-Net-based model, which explicitly encodes critical scene objects (vehicles) in a graph representation, leading to further performance improvements over several state-of-the-art visual attention prediction methods. The dataset, code and models will be publicly released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13218 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ObjectVisA-120: Object-based Visual Attention Prediction in Interactive Street-crossing Environments Vozniak, Igor Mueller, Philipp Lipp, Nils Sprenger, Janis Poddubnyy, Konstantin Hovhannisyan, Davit Mueller, Christian Bulling, Andreas Slusallek, Philipp Computer Vision and Pattern Recognition The object-based nature of human visual attention is well-known in cognitive science, but has only played a minor role in computational visual attention models so far. This is mainly due to a lack of suitable datasets and evaluation metrics for object-based attention. To address these limitations, we present ObjectVisA-120 -- a novel 120-participant dataset of spatial street-crossing navigation in virtual reality specifically geared to object-based attention evaluations. The uniqueness of the presented dataset lies in the ethical and safety affiliated challenges that make collecting comparable data in real-world environments highly difficult. ObjectVisA-120 not only features accurate gaze data and a complete state-space representation of objects in the virtual environment, but it also offers variable scenario complexities and rich annotations, including panoptic segmentation, depth information, and vehicle keypoints. We further propose object-based similarity (oSIM) as a novel metric to evaluate the performance of object-based visual attention models, a previously unexplored performance characteristic. Our evaluations show that explicitly optimising for object-based attention not only improves oSIM performance but also leads to an improved model performance on common metrics. In addition, we present SUMGraph, a Mamba U-Net-based model, which explicitly encodes critical scene objects (vehicles) in a graph representation, leading to further performance improvements over several state-of-the-art visual attention prediction methods. The dataset, code and models will be publicly released. |
| title | ObjectVisA-120: Object-based Visual Attention Prediction in Interactive Street-crossing Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.13218 |