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A vision-only driving model, distilled from a massive VLA teacher, not only matches but *exceeds* its teacher's performance, proving that there's still headroom in vision-centric architectures for autonomous driving.
Radar point clouds, when enriched with spectral information, can outperform traditional dense range-Doppler spectra, suggesting a path toward more robust and generalizable radar perception models.
Ditch the computational bloat: DeltaWorld slashes parameters by 35x and FLOPs by 2000x while generating more realistic video futures.
Get 3x faster image segmentation and comparable video segmentation performance to fine-tuned models, all while keeping your vision encoder frozen.
Ditch the complex trackers: a plain ViT encoder, augmented with a clever query propagation trick, delivers state-of-the-art video segmentation at 10x the speed.
