: Microsoft research highlighted the move from sparse (e.g., 68 points) to dense landmarks to better capture subtle expressions and facial identity.
: A simple and effective association method that improved tracking by associating almost every detection box, including those with low scores that were previously discarded. This approach significantly reduced fragmented trajectories and missing objects. Computer Vision ECCV 2022: 17th European Confe...
: Features like BEVFormer used spatiotemporal transformers to learn unified BEV representations from multi-camera images, which is a critical advancement for autonomous driving perception. : Microsoft research highlighted the move from sparse (e