TL;DR
The findings indicate that while event cameras do not yet surpass strong RGB transformer pipelines, they constitute an efficient and complementary modality for violence understanding.
Video-level AUC here is thus a composite of event evidence and pre-event source cues, a shared source of discrimination that can obscure differences between representations.
This work introduces Emo-DVS, the first large-scale event-based emotion analysis dataset, which couples dynamic illumination with the Facial Action Unit (FAU) subset and emotion subset and proposes the Information-Guided Gated Fusion (IGF) framework, which achieves state-of-the-art performance.
The proposed ACTAF-Net provides an adaptive spatiotemporal representation by jointly exploiting multi-scale spatial cues, complementary temporal dependencies, cross-temporal interactions, and reliability-aware feature fusion for robust surveillance-based crime recognition.
Low-latency violent event detection is essential for intelligent edge surveillance. Current violent recognition models suffer three critical deployment drawbacks: prohibitive computational overhead from full fine‑tuning, inadequate temporal modeling for continuous video streams, and severe performance degradation whe...
This paper introduces EventKitchen, a large-scale stereo event camera benchmark dataset of human cooking activities in the kitchen that establishes a challenging benchmark for neuromorphic vision beyond autonomous driving.
Experimental findings indicate that the hierarchical integration of convolutional and recurrent layers with attention significantly improves recognition accuracy while maintaining a computationally lightweight architecture suitable for near-real-time deployment.
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