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Sparse annotations can yield results nearly indistinguishable from dense ones, with SA-VIS achieving over 1% improvement in AP on state-of-the-art benchmarks.
Get up to 20% faster ViT inference by hot-swapping certain attention heads for depthwise convolutions – without tanking accuracy.
Bridging the gap between third-person and first-person video generation is as simple as interpolating the videos, revealing that spatio-temporal discontinuities are the real bottleneck.
Removing an object from a video isn't just about inpainting what's behind it; VOID ensures the *downstream physics* still make sense.
Autonomous vehicles can now better identify the unexpected, thanks to a new method that boosts out-of-distribution detection by up to 20% without retraining.
The field of video understanding is rapidly shifting from isolated pipelines to unified models capable of adapting to diverse downstream tasks, demanding a re-evaluation of current approaches.
