Pixel attention mechanismChannel attentionSpatial attentionDeep learningWe propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Single-image super-resolution reconstruction technology is to reconstruct fuzzy low-resolution images into clearer high-resolution ...
Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis...
1Tags Code Repository files navigation README PAN [:zap: 272K parameters] Lowest parameters in AIM2020 Efficient Super Resolution. Paper|Video Efficient Image Super-Resolution Using Pixel Attention Authors: Hengyuan Zhao,Xiangtao Kong,Jingwen He,Yu Qiao,Chao Dong ...
Channel Pixel Attention The operation can be simply embedded with CNNs. Apart from the place in the above picture. A further data flow explanation can be seen in ./models/upanets,.py's CPA function. And, a demonstration of setting sc_x is under blew: *same=False: This scenario can ...
image super-resolution reconstruction algorithm—Swin Transformer with a Vast-receptive-field Pixel Attention, which combines the vast-receptive-field pixel attention mechanism with the Swin Transformer self-attention mechanism, focuses on the learning of the high-frequency information features of the image...
基于双重注意力机制,本文针对Pixel-wise regression的任务,提出了一种更加精细的双重注意力机制——极化自注意力(Polarized Self-Attention)。作为一个即插即用的模块,在人体姿态估计和语义分割任务上,作者将它用在了以前的SOTA模型上,并达到了新的SOTA性能,霸榜COCO人体姿态估计和Cityscapes语义分割。
We address this problem with heuristic attention pixel-level contrastive loss for representation learning (HAPiCLR), a self-supervised joint embedding contrastive framework that operates at the pixel level and makes use of heuristic mask information. HAPiCLR leverages pixel-level information from the ...
In this paper, we present the Polarized Self-Attention(PSA) block that incorporates two critical designs towards high-quality pixel-wise regression: (1) Polarized filtering: keeping high internal resolution in both channel and spatial attention computation while completely collapsing input tensors along...
we propose a novel pixel-wise contextual attention network,i.e., the PiCANet, to learn to selectively attend to informa-tive context locations for each pixel. Specif i cally, for eachpixel, it can generate an attention map in which each atten-tion weight corresponds to the contextual ...
3.3. Pixel Attention Module (PAM) Although the CAM calculated in Section 3.1 can accurately cover the most discriminative regions in the target object, CAMs can only define parts of objects, rather than the whole region. To resolve CAM under-activation, we introduce the Pixel Attention Module. ...