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Search: “DINOv2”

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Canadian Start-up smartARM Uses AI to Create Intuitive Bionic Prosthetics

Toronto startup smartARM built a bionic prosthetic arm using Meta's DINOv2 vision model and AI glasses to automatically select grips for objects.

Toronto-based smartARM developed a vision-first bionic arm that uses a palm-embedded camera and Meta's open-source DINOv2 model to recognize objects from a few reference photos and automatically select suitable grips. It integrates Meta AI Glasses and the Meta Wearables Device Access Toolkit for additional egocentric context, letting users add new objects via a phone app. The arm adapts to new objects almost instantly instead of the weeks previously required, and is used by former NFL player Shaquem Griffin.

Meta Newsroomupdated · 13h agofirst · 14h agoAI industry 2 sources

Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models

QPriv-VL prunes privacy-sensitive visual tokens in federated/split VQA, cutting membership-inference success on VQA-RAD from 0.99 to 0.76-0.79 using ~40% of tokens.

The paper proposes QPriv-VL, a question-guided token-pruning framework for federated, split, and U-shaped split learning that suppresses privacy-sensitive visual patches before transmission. Its Dynamic Threshold Predictor combines cross-modal question relevance with frozen DINOv2-derived sensitivity to compute a per-sample pruning ratio and retention mask in one forward pass, without sensitivity labels. Evaluated on GQA, OK-VQA, VQAv2, SLAKE, VQA-RAD, and PathVQA against FSHA, FORA, iDLG, and attribute-inference membership inference attacks, it matches or beats fixed-ratio pruning. On VQA-RAD it reduces membership-inference success from 0.99 to 0.76-0.79 while preserving competitive accuracy with about 40% of the original token budget.

arXiv cs.CR · 2d agoResearch