Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement
arXiv:2412.01282v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile devices also arises, such as the AI assistant software. Some efforts try to migrate VLMs to edge devices to expand their application scope. Simplifying the model structure is a common method, but as the model shrinks, the trade-off between performance and size becomes more and more difficult. Knowledge distillation (KD) can help models improve comprehensive ca
The increasing demand for powerful AI on mobile devices and the inherent performance-size trade-offs in shrinking models necessitate new optimization techniques like knowledge distillation.
This development indicates progress in making powerful Vision-Language Models (VLMs) more accessible and efficient for edge devices, expanding their practical applications.
The ability to distill complex cross-modal alignment knowledge into smaller models means robust VLM capabilities can be deployed where previously impossible due to computational constraints.
- · Mobile device manufacturers
- · On-device AI developers
- · Consumers of AI assistant software
- · Edge computing infrastructure
- · Companies relying solely on cloud-based VLM processing
- · Developers neglecting model efficiency for edge deployment
More sophisticated and real-time AI capabilities become available on smartphones and other portable devices.
Demand for specialized AI hardware optimized for efficient on-device inference will likely increase.
The proliferation of advanced on-device AI could lead to new privacy models as less data needs to be sent to the cloud for processing.
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Read at arXiv cs.AI