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VARestorer distills a text-to-image VAR model into a one-step super-resolution network, achieving state-of-the-art image quality with a 10x speedup.
By unifying generative and discriminative approaches, UniGenDet achieves superior image generation and detection, suggesting that these tasks benefit from a symbiotic relationship previously hindered by architectural divergence.
Legged robots can now recover from sensor noise and crazy user commands with 10x greater reliability, thanks to a new method that respects the robot's competence boundaries.
Binarizing weights and ternarizing activations in Transformers can deliver 16-24x kernel speedup and comparable accuracy to full-precision models, finally making ultra-low-bit quantization practical.
Ditch language descriptions: this new driving model leverages dense 3D geometry for superior autonomous driving performance and cross-camera generalization.
Generative videos might look great, but a new metric reveals they often suffer from jarring 3D spatial inconsistencies that existing metrics miss.
DriveTok achieves unified multi-view reconstruction and understanding by learning scene tokens that integrate semantic, geometric, and textural information, outperforming existing 2D tokenizers in autonomous driving scenarios.
