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DINO-VO's learned patch selection and differentiable bundle adjustment leapfrogs traditional heuristic feature extraction, achieving SOTA monocular visual odometry with impressive generalization.
Image generation models can now achieve state-of-the-art fidelity with up to 64x fewer tokens, thanks to a novel masking strategy that prevents latent space collapse.
