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LingBot Vision is a new visual foundation model released under Apache 2.0, available in four sizes (small, base, large, giant) and pretrained with masked boundary modeling. It powers a depth estimation system that tops multiple benchmarks.
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.
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.
LingBot-Vision introduces masked boundary modeling for self-supervised pretraining, achieving a 0.296 RMSE on NYUv2 linear-probe with 1.1B parameters versus 0.309 for DINOv3-7B, though it trails on ImageNet; weights are released in four sizes.