OMIT 发表于 2025-3-27 00:37:19
0302-9743 , Japan, in November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, iANTIC 发表于 2025-3-27 01:32:21
http://reply.papertrans.cn/24/2342/234127/234127_32.png热烈的欢迎 发表于 2025-3-27 07:10:50
https://doi.org/10.1007/978-3-319-26047-1iate layers. In this way, GFFRB can enjoy the merits of the lightweight of the group convolution and the high-efficiency of the skip connections, thus achieving better performance compared with most current residual blocks. Experiments on the benchmark test sets show that our models are more efficient than most of the state-of-the-art methods.myalgia 发表于 2025-3-27 11:28:23
Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention NetworkCAB). Several models of different sizes are released to meet various practical requirements. Extensive benchmark experiments show that the proposed models achieve better performance with much fewer multiply-adds and parameters (Source code is at .).BORE 发表于 2025-3-27 17:19:25
An Efficient Group Feature Fusion Residual Network for Image Super-Resolutioniate layers. In this way, GFFRB can enjoy the merits of the lightweight of the group convolution and the high-efficiency of the skip connections, thus achieving better performance compared with most current residual blocks. Experiments on the benchmark test sets show that our models are more efficient than most of the state-of-the-art methods.我的巨大 发表于 2025-3-27 18:50:42
http://reply.papertrans.cn/24/2342/234127/234127_36.png撤退 发表于 2025-3-27 23:53:51
http://reply.papertrans.cn/24/2342/234127/234127_37.pngEmg827 发表于 2025-3-28 04:48:30
https://doi.org/10.1007/978-1-349-03555-7ith non-stationary textures remains a challenging task for computer vision. In this paper, a novel approach to image inpainting problem is presented, which adapts exemplar-based methods for deep convolutional neural networks. The concept of . is introduced with the purpose of preserving feature cont反话 发表于 2025-3-28 07:38:33
http://reply.papertrans.cn/24/2342/234127/234127_39.png残忍 发表于 2025-3-28 10:41:56
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