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Adapting Large Visual-Language Models to Edge Devices for Diverse Modalities


Core Concepts
The author introduces EdgeVL, a framework that adapts large Vision-Language models for edge devices, addressing challenges in diverse visual modalities and computational constraints. The approach integrates dual-modality knowledge distillation and quantization-aware contrastive learning to enhance model efficiency.
Abstract
The content discusses the challenges of deploying Vision-Language models on edge devices and introduces EdgeVL as a solution. It highlights the importance of adapting large models for diverse visual modalities without manual annotations, showcasing significant accuracy improvements and efficiency enhancements. Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain. The introduction of EdgeVL bridges this gap by seamlessly integrating dual-modality knowledge distillation and quantization-aware contrastive learning. This approach enables the adaptation of large VL models for efficient use with both RGB and non-RGB images on resource-limited devices without the need for manual annotations. In recent years, there has been a surge of interest in the development of Vision-Language (VL) models capable of conducting integrated reasoning across visual and textual data. Prominent large-scale VL models typically employ distinct visual and text encoders to enable direct comparison across two modalities. An edge device often comes equipped with multiple sensors beyond standard RGB cameras, such as depth sensors and infrared cameras. Despite this, most large VL models are tailored to RGB images, leaving adaptability to alternative inputs largely unexplored. To overcome these challenges, a novel framework is needed that can adapt the VL embedding prowess of large models to non-RGB images without relying on human annotations while minimizing its computational footprint to suit edge device capabilities. The proposed EdgeVL framework integrates dual-modality knowledge distillation with quantization-aware training to optimize model efficiency for open-vocabulary classification tasks across various visual modalities on resource-constrained edge devices. EdgeVL represents a systematic effort to adapt large VL models for edge deployment, showcasing accuracy improvements on multiple datasets and reduction in model size up to 93-fold.
Stats
Model size=5213MB Model size=56MB Accuracy improvements up to 15.4% Reduction in model size up to 93-fold
Quotes
"EdgeVL is the first framework to systematically address the adaptation of large VL models for edge devices." "We introduce a method to transfer visual language alignment from pre-trained VL models to compact visual models for both RGB and non-RGB images."

Deeper Inquiries

How can EdgeVL's approach be applied beyond edge devices?

EdgeVL's approach of seamlessly integrating dual-modality knowledge distillation and quantization-aware contrastive learning can have applications beyond edge devices. One potential application is in the field of autonomous vehicles, where large visual-language models need to process diverse visual modalities efficiently. By adapting these models for use with different sensors like LiDAR, radar, and cameras, autonomous vehicles can enhance their perception capabilities and make more informed decisions on the road. Additionally, in healthcare settings, where medical imaging data comes in various forms such as X-rays, MRIs, and CT scans, EdgeVL's approach could enable efficient processing of this multimodal data for accurate diagnosis and treatment planning.

What counterarguments exist against adapting large VL models for edge deployment?

One counterargument against adapting large Vision-Language (VL) models for edge deployment is the potential trade-off between model complexity and computational efficiency. Large VL models are known to be computationally intensive due to their size and architecture requirements. Adapting these models for resource-constrained edge devices may lead to compromises in accuracy or speed due to limited processing power or memory capacity on these devices. Another counterargument could be related to privacy concerns since deploying sophisticated VL models on edge devices may raise questions about data security and user privacy if sensitive information is processed locally without proper safeguards.

How does the concept of automated dataset curation impact future developments in machine learning?

The concept of automated dataset curation has significant implications for future developments in machine learning by streamlining the data preprocessing pipeline and reducing manual intervention required for dataset preparation. Automated dataset curation leverages AI algorithms to select relevant samples from a pool of unlabeled data based on predefined criteria or objectives. This not only saves time but also ensures that training datasets are optimized for specific tasks or domains without bias introduced by human annotation errors. In the context of EdgeVL's automated sample selection mechanism using ChatGPT-4 engine to curate a label superset from open-vocabulary features demonstrates how AI-driven approaches can enhance model training efficiency across diverse visual modalities without relying on manual annotations. This automation paves the way for scalable development of robust machine learning systems capable of handling complex real-world scenarios with minimal human involvement while maintaining high performance standards.
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