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DINOv3 a paper by meta, a significant advancement in self-supervised learning (SSL) for computer vision, emphasizing its ability to create robust and versatile visual representations without relying on extensive human annotations. The research highlights improvements in dense feature maps through a novel "Gram anchoring" strategy, which addresses the issue of performance degradation in dense tasks during extended training. DINOv3 demonstrates state-of-the-art performance across various computer vision applications, including object detection, semantic segmentation, and depth estimation, even outperforming models with supervised pre-training. Furthermore, the paper showcases the generality of DINOv3 by applying its training recipe to geospatial data, achieving strong results on satellite imagery. The text also acknowledges the environmental impact of training such large-scale models and discusses the effective distillation of knowledge from larger 7-billion parameter models into smaller, more efficient variants.