OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
Muhammad Rameez Ur Rahman, Jhony H. Giraldo, Indro Spinelli,, St\'ephane Lathuili\`ere, Fabio Galasso

TL;DR
This paper introduces OVOSE, a novel open-vocabulary semantic segmentation method for event cameras that leverages synthetic data and knowledge distillation, outperforming existing closed-set models in driving scenarios.
Contribution
The paper presents the first open-vocabulary segmentation algorithm for event cameras, combining synthetic data and knowledge distillation from image models.
Findings
OVOSE outperforms existing methods on DDD17 and DSEC-Semantic datasets.
It demonstrates superior open-vocabulary segmentation in event-based data.
The approach effectively transfers knowledge from image-based models to event cameras.
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…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Machine Learning and ELM
MethodsKnowledge Distillation
