CoLoRSMamba: Conditional LoRA-Steered Mamba for Supervised Multimodal Violence Detection
📰 ArXiv cs.AI
CoLoRSMamba is a multimodal architecture for violence detection that combines video and audio modalities using conditional LoRA-steered Mamba
Action Steps
- Combine video and audio modalities using a directional Video to Audio architecture
- Use CLS-guided conditional LoRA to adapt AudioMamba projections
- Implement channel-wise modulation vectors and stabilization gates to selectively focus on relevant audio features
- Evaluate the performance of CoLoRSMamba on supervised multimodal violence detection tasks
Who Needs to Know This
This research benefits AI engineers and researchers working on multimodal violence detection, as it provides a novel approach to combining video and audio modalities for improved detection accuracy. The team can apply this architecture to develop more effective violence detection systems.
Key Insight
💡 Conditional LoRA-steered Mamba can effectively combine video and audio modalities for improved violence detection accuracy
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💡 CoLoRSMamba: A novel multimodal architecture for violence detection using conditional LoRA-steered Mamba #AI #MultimodalLearning
Key Takeaways
CoLoRSMamba is a multimodal architecture for violence detection that combines video and audio modalities using conditional LoRA-steered Mamba
Full Article
Title: CoLoRSMamba: Conditional LoRA-Steered Mamba for Supervised Multimodal Violence Detection
Abstract:
arXiv:2604.03329v1 Announce Type: cross Abstract: Violence detection benefits from audio, but real-world soundscapes can be noisy or weakly related to the visible scene. We present CoLoRSMamba, a directional Video to Audio multimodal architecture that couples VideoMamba and AudioMamba through CLS-guided conditional LoRA. At each layer, the VideoMamba CLS token produces a channel-wise modulation vector and a stabilization gate that adapt the AudioMamba projections responsible for the selective st
Abstract:
arXiv:2604.03329v1 Announce Type: cross Abstract: Violence detection benefits from audio, but real-world soundscapes can be noisy or weakly related to the visible scene. We present CoLoRSMamba, a directional Video to Audio multimodal architecture that couples VideoMamba and AudioMamba through CLS-guided conditional LoRA. At each layer, the VideoMamba CLS token produces a channel-wise modulation vector and a stabilization gate that adapt the AudioMamba projections responsible for the selective st
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