EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond

• URL: http://arxiv.org/abs/2411.18328v1
• Date: Wed, 27 Nov 2024 13:28:57 GMT
• Title: EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
• Authors: Meiqi Cao, Xiangbo Shu, Jiachao Zhang, Rui Yan, Zechao Li, Jinhui Tang,
• Abstract summary: Event-based Action Recognition (EAR) possesses the advantages of high-temporal and privacy preservation compared with traditional action recognition. We present EventCrab, a framework that adeptly integrates the "lighter" frame-specific networks for dense event frames with the "heavier" point-specific networks for sparse event points. Experiments on four datasets demonstrate the significant performance of our proposed EventCrab.
• Score: 61.10181853363728
• License:
• Abstract: Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current leading EAR solutions typically follow two regimes: project unconstructed event streams into dense constructed event frames and adopt powerful frame-specific networks, or employ lightweight point-specific networks to handle sparse unconstructed event points directly. However, such two regimes are blind to a fundamental issue: failing to accommodate the unique dense temporal and sparse spatial properties of asynchronous event data. In this article, we present a synergy-aware framework, i.e., EventCrab, that adeptly integrates the "lighter" frame-specific networks for dense event frames with the "heavier" point-specific networks for sparse event points, balancing accuracy and efficiency. Furthermore, we establish a joint frame-text-point representation space to bridge distinct event frames and points. In specific, to better exploit the unique spatiotemporal relationships inherent in asynchronous event points, we devise two strategies for the "heavier" point-specific embedding: i) a Spiking-like Context Learner (SCL) that extracts contextualized event points from raw event streams. ii) an Event Point Encoder (EPE) that further explores event-point long spatiotemporal features in a Hilbert-scan way. Experiments on four datasets demonstrate the significant performance of our proposed EventCrab, particularly gaining improvements of 5.17% on SeAct and 7.01% on HARDVS.

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