Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution Zhewei Huang1 Ailin Huang1 Xiaotao Hu1,2 Chen Hu1 Jun Xu2,3,∗ Shuchang Zhou1,∗ 1Megvii Technology 2Nankai University 3Guangdong Provincial Key Laboratory of Big Data ...
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution 26 Oct 2023 · Zhewei Huang, Ailin Huang, Xiaotao Hu, Chen Hu, Jun Xu, Shuchang Zhou · Edit social preview The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by...
MIT license YouTube|Poster|Enhancement Model|demo|中文介绍 Introduction We want to increase video resolution and frame rates end-to-end (end-to-end STVSR). This project is the implement ofScale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution. Our SAFA network outperform...
Adaptive feature extractionMulti-scale features aggregationFeature fusionDeep supervisionAccurate retinal vessel segmentation is crucial for early clinical diagnosis and effective disease treatment guidance. Due to the large scale variation and complex structure of retinal vessels, common U-shaped networks fail...
, the model pays more attention to contextual features. Secondly, the result of feature-based aggregation for each partition is obtained, and the four partition features are merged in the channel dimension. $$ F_{m}^{\prime} = \left[ {m_{1}^{\prime} ,m_{2}^{\prime} ,m_{3}^{...
Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 2117–2125. [Google Scholar] Liu, S.; Qi, L.; Qin, H.; Shi, J.; Jia, J. Path aggregation network for instance ...
Finally, the multi-level features are fused by the feature aggregation module, and the multi-level feature pyramid is obtained for the final stage prediction. The feature refinement module is actually a U-shaped network. C. Zhu et al. put forward that FSAF module allows each instance to ...
Multi-scale context aggregation by di- lated convolutions. In ICLR, 2016. [31] M. D. Zeiler, G. W. Taylor, and R. Fergus. Adaptive decon- volutional networks for mid and high level feature learning. In ICCV, 2011. [32] R. Zhang, S. Tang, M. Lin, J. Li, and S. Yan. ...
180b,180cmay also provide mobility management functions, such as handoff triggering, tunnel establishment, radio resource management, traffic classification, quality of service (QoS) policy enforcement, and the like. The ASN gateway182may serve as a traffic aggregation point and may be responsible ...
Dense Cross-Scale Feature Aggregation U-Net: This approach breaks through the limitations of traditional U-Net’s unidirectional feature transmission by designing a high-dimensional densely nested structure, which is a variant of U-Net++. Through a cross-layer multi-path feature interaction mechanism...