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Shared codebooks and fused-teacher self-distillation offer a surprisingly effective approach to multi-view multi-label classification when data is incomplete.
Ditch the expensive 3D annotations: Reliev3R trains high-quality 3D reconstruction models from scratch using only monocular relative depths and sparse image correspondences.
Pre-training with Dual Latent World Models unlocks significant performance gains in autonomous driving tasks by learning holistic Gaussian-centric representations.
