ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting

• URL: http://arxiv.org/abs/2502.14780v1
• Date: Thu, 20 Feb 2025 18:01:41 GMT
• Title: ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting
• Authors: Abhijit Mishra, Richard Noh, Hsiang Fu, Mingda Li, Minji Kim,
• Abstract summary: This paper explores Visual Instruction Rewriting, a novel approach that transforms multimodal instructions into text-only commands. We present a dataset of over 39,000 examples across 14 domains and develop a compact VLM, pretrained on image captioning datasets. Experimental results, evaluated through NLG metrics such as BLEU, METEOR, and ROUGE, demonstrate that even a quantized version of the model can achieve effective instruction rewriting.
• Score: 5.657347835913079
• License:
• Abstract: Efficient and privacy-preserving multimodal interaction is essential as AR, VR, and modern smartphones with powerful cameras become primary interfaces for human-computer communication. Existing powerful large vision-language models (VLMs) enabling multimodal interaction often rely on cloud-based processing, raising significant concerns about (1) visual privacy by transmitting sensitive vision data to servers, and (2) their limited real-time, on-device usability. This paper explores Visual Instruction Rewriting, a novel approach that transforms multimodal instructions into text-only commands, allowing seamless integration of lightweight on-device instruction rewriter VLMs (250M parameters) with existing conversational AI systems, enhancing vision data privacy. To achieve this, we present a dataset of over 39,000 examples across 14 domains and develop a compact VLM, pretrained on image captioning datasets and fine-tuned for instruction rewriting. Experimental results, evaluated through NLG metrics such as BLEU, METEOR, and ROUGE, along with semantic parsing analysis, demonstrate that even a quantized version of the model (<500MB storage footprint) can achieve effective instruction rewriting, thus enabling privacy-focused, multimodal AI applications.

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