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FedCC proposes a federated learning framework combining a frozen DINOv2 backbone with a lightweight YOLO detection head and LoRA modules for robust corpus callosum localization in fetal ultrasound images, achieving strong performance with greatly reduced communication cost in a multi-center setting.
The paper introduces RiT, a vanilla Diffusion Transformer trained on frozen DINOv2 features using flow matching with x-prediction, achieving competitive FID scores on ImageNet 256×256 with fewer parameters and fast sampling without distillation.
MARCO introduces a compact, fast model for semantic correspondence that achieves state-of-the-art accuracy and generalization to unseen keypoints using a coarse-to-fine objective and self-distillation framework with DINOv2.