arXiv:2604.03329v2 Announce Type: replace-cross Abstract: Automatic violence detection from video is challenging because violent interactions may be distant, occluded, or only partially visible. Audio can provide complementary evidence for violent events that are difficult to recognize from visual information alone. However, audio itself may be absent, dubbed, or dominated by environmental noise, making the central challenge not whether to incorporate audio but how to adapt reliance on it according to the visual scene. We introduce \emph{AViS-Mamba}, an audiovisual Mamba-based architecture in
The continuous advancements in AI, particularly Mamba architectures and multimodal learning, are enabling more sophisticated real-time threat detection capabilities.
This development represents improved accuracy in automated violence detection, which has significant implications for public safety, surveillance systems, and content moderation.
AI systems can now adaptively weigh visual and audio cues for violence detection, leading to more robust and less error-prone classifications in challenging real-world scenarios.
- · Security and surveillance sectors
- · Social media platforms (content moderation)
- · AI algorithm developers (multimodal AI)
- · Criminal elements operating in public spaces
- · Systems relying solely on visual violence detection
Enhanced real-time monitoring and quicker intervention in violent incidents.
Increased demand for ethically developed and transparent AI systems for public safety applications.
Potential for privacy concerns and public debate surrounding pervasive AI-powered surveillance technologies.
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Read at arXiv cs.AI