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This paper introduces CW-BASS v2, a saturation-aware pseudo-label selection method for semi-supervised semantic segmentation that adaptively switches between strict filtering and an adaptive confidence floor depending on the teacher's reliability. It shows improved results over baselines across several benchmarks with DINOv2 teachers.
A researcher reports a surprising 50-point accuracy gap between frozen SigLIP2 (92%) and DINOv2 (41%) embeddings on a fine-grained car classification task using k-NN, seeking insight on whether a linear probe would close the gap or if DINOv2 is unsuited for retrieval.
The UK government is utilizing Meta's DINOv2 model to optimize reforestation efforts, aiming to reduce costs and improve access to greenspaces.
Orakl Oncology is leveraging the DINOv2 model to integrate machine learning with experimental insights, aiming to accelerate cancer treatment discovery and drug development.
Depth Anything V2 is a monocular depth estimation model that significantly outperforms V1 in fine-grained details and robustness, offering faster inference and higher accuracy than SD-based models. It is available on Replicate under varying licenses.