OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
<|reference_start|>OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras: Event cameras, known for low-latency operation and superior performance in challenging lighting conditions, are suitable for sensitive computer vision tasks such as semantic segmentation in autonomous driving. However, challenges arise due to limited event-based data and the absence of large-scale segmentation benchmarks. Current works are confined to closed-set semantic segmentation, limiting their adaptability to other applications. In this paper, we introduce OVOSE, the first Open-Vocabulary Semantic Segmentation algorithm for Event cameras. OVOSE leverages synthetic event data and knowledge distillation from a pre-trained image-based foundation model to an event-based counterpart, effectively preserving spatial context and transferring open-vocabulary semantic segmentation capabilities. We evaluate the performance of OVOSE on two driving semantic segmentation datasets DDD17, and DSEC-Semantic, comparing it with existing conventional image open-vocabulary models adapted for event-based data. Similarly, we compare OVOSE with state-of-the-art methods designed for closed-set settings in unsupervised domain adaptation for event-based semantic segmentation. OVOSE demonstrates superior performance, showcasing its potential for real-world applications. The code is available at https://github.com/ram95d/OVOSE.<|reference_end|>
@article{rahman2024ovose:,
    title={OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras},
    author={Muhammad Rameez Ur Rahman, Jhony H. Giraldo, Indro Spinelli,
  St'ephane Lathuili`ere, Fabio Galasso},
    journal={arXiv preprint arXiv:2408.09424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09424},
    primaryClass={cs.CV}
}