@AdinaYakup: LingBot Vision A self-supervised vision backbone family for dense spatial perception from Ant Group @robbyant_brain - A…

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Summary

LingBot Vision, a self-supervised vision backbone family from Ant Group, uses masked boundary modeling to achieve state-of-the-art performance on dense spatial perception tasks, beating the larger DINOv3 model on NYU-Depth v2.

LingBot Vision 👀🤖 A self-supervised vision backbone family for dense spatial perception from Ant Group @robbyant_brain - Apache2.0 - ViT-S to ViT-g - 1.1B model beats the 7B DINOv3 on NYU-Depth v2 (self-reported) 💡 - Pretrained with masked boundary modeling: keeps features https://t.co/T7nnbySR4w
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LingBot Vision 👀🤖 A self-supervised vision backbone family for dense spatial perception from Ant Group @robbyant_brain

  • Apache2.0
  • ViT-S to ViT-g
  • 1.1B model beats the 7B DINOv3 on NYU-Depth v2 (self-reported) 💡
  • Pretrained with masked boundary modeling: keeps features https://t.co/T7nnbySR4w

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Meta ships DINOv3 behind an access gate under its own license. Ant's Robbyant just shipped a full vision backbone family under Apache-2.0. What happens when perception goes free and small?

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Robbyant, an embodied AI company under Ant Group, released LingBot-Vision, a self-supervised vision backbone family ranging from 21M to 1.1B parameters, under Apache-2.0. It matches or beats DINOv3 on several depth and segmentation benchmarks despite using less than one third of the training data, highlighting a push for open perception models.