DeepLearning ru:
Clockwork Convnets for Video Semantic Segmentation.
Adaptive video processing by incorporating data-driven clocks.
We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video.
https://arxiv.org/pdf/1608.03609v1.pdf
https://github.com/shelhamer/clockwork-fcn
http://www.gitxiv.com/posts/89zR7ATtd729JEJAg/clockwork-convnets-for-video-semantic-segmentation
#dl #CV #Caffe #video #Segmentation
Clockwork Convnets for Video Semantic Segmentation.
Adaptive video processing by incorporating data-driven clocks.
We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video.
https://arxiv.org/pdf/1608.03609v1.pdf
https://github.com/shelhamer/clockwork-fcn
http://www.gitxiv.com/posts/89zR7ATtd729JEJAg/clockwork-convnets-for-video-semantic-segmentation
#dl #CV #Caffe #video #Segmentation
GitHub
shelhamer/clockwork-fcn
Clockwork Convnets for Video Semantic Segmenation. Contribute to shelhamer/clockwork-fcn development by creating an account on GitHub.