OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras

• URL: http://arxiv.org/abs/2408.09424v1
• Date: Sun, 18 Aug 2024 09:56:32 GMT
• Title: OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
• Authors: Muhammad Rameez Ur Rahman, Jhony H. Giraldo, Indro Spinelli, Stéphane Lathuilière, Fabio Galasso,
• Abstract summary: We introduce OVOSE, the first Open-Vocabulary Semantic algorithm for Event cameras. We evaluate OVOSE on two driving semantic segmentation datasets DDD17, and DSEC-Semantic. OVOSE demonstrates superior performance, showcasing its potential for real-world applications.
• Score: 18.07403094754705
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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