eeVAHN

eeVAHN, short for energy-efficient Visual-Audio Hierarchical Network, is a family of multimodal neural networks designed for real-time processing of audio and visual streams on edge devices. It emphasizes data efficiency, low latency, and reduced energy consumption while maintaining competitive accuracy on standard benchmarks.

Architecture: The model uses separate lightweight encoders for visual and audio inputs, followed by a series of cross-modal fusion blocks arranged hierarchically across temporal scales. It employs attention mechanisms to align features across modalities, and uses model compression techniques such as quantization to INT8, pruning, and knowledge distillation. The design aims to minimize FLOPs and memory footprint while preserving performance. The system supports streaming inference and can adapt to varying frame rates and sampling rates.

Training and evaluation: eeVAHN is trained on curated audiovisual datasets including VGGSound and AudioSet, and evaluated on tasks such as event detection, action recognition from video with sound, and audiovisual speech enhancement. In comparisons with single-modality baselines and larger multimodal models, eeVAHN demonstrates favorable efficiency-accuracy trade-offs on constrained hardware.

Applications and limitations: Potential uses include mobile apps for content tagging, assistive devices, smart cameras, and real-time media analysis. Limitations include dependency on synchronized multimodal input, sensitivity to domain shift, and potential degradation under aggressive compression or occlusions. Generalization to unseen environments remains an active area of research.

See also: Multimodal learning, Edge AI, Efficient neural networks.