Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models CVPR2023
CVPR 2023: Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models by Paul Micaelli (Edin, NVIDIA), Arash Vahdat (NVIDIA), Hongxu Yin (NVIDIA), Jan Kautz (NVIDIA), Pavlo Molchanov (NVIDIA).
We apply and extend a deep equilibrium model (DEQ) to the task of keypoints estimation. This network conducts an infinite number of iterations with self-conditioning, where the input is the output of the previous iteration, until a stopping criterion is met. It learns to iteratively enhance initial estimates. In our work, we further extend this network architecture to enable landmark estimation in videos for free (training on single images). We observe two key outcomes: (i) a significant reduction in computation as frames exhibit high similarity, and (ii) an improvement in landmark stability.
Project page: https://github.com/NVlabs/LDEQ_RwR/
Видео Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models CVPR2023 канала NVIDIA Developer
We apply and extend a deep equilibrium model (DEQ) to the task of keypoints estimation. This network conducts an infinite number of iterations with self-conditioning, where the input is the output of the previous iteration, until a stopping criterion is met. It learns to iteratively enhance initial estimates. In our work, we further extend this network architecture to enable landmark estimation in videos for free (training on single images). We observe two key outcomes: (i) a significant reduction in computation as frames exhibit high similarity, and (ii) an improvement in landmark stability.
Project page: https://github.com/NVlabs/LDEQ_RwR/
Видео Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models CVPR2023 канала NVIDIA Developer
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