Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities

• URL: http://arxiv.org/abs/2403.04908v3
• Date: Tue, 01 Oct 2024 14:22:15 GMT
• Title: Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
• Authors: Kaiwen Cai, Zhekai Duan, Gaowen Liu, Charles Fleming, Chris Xiaoxuan Lu,
• Abstract summary: Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices. We introduce EdgeVL, a novel framework that seamlessly integrates dual-modality knowledge distillation and quantization-aware contrastive learning. Our work represents the first systematic effort to adapt large VL models for edge deployment, showcasing up to 15.4% accuracy improvements on multiple datasets and up to 93-fold reduction in model size.
• Score: 11.53488611812612
• License:
• Abstract: 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. We introduce EdgeVL, a novel framework that bridges this gap by seamlessly integrating dual-modality knowledge distillation and quantization-aware contrastive learning. This approach enables the adaptation of large VL models, like CLIP, for efficient use with both RGB and non-RGB images on resource-limited devices without the need for manual annotations. EdgeVL not only transfers visual language alignment capabilities to compact models but also maintains feature quality post-quantization, significantly enhancing open-vocabulary classification performance across various visual modalities. Our work represents the first systematic effort to adapt large VL models for edge deployment, showcasing up to 15.4% accuracy improvements on multiple datasets and up to 93-fold reduction in model size.

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