According to the preliminary results of AIM 2020 Real Image Super-Resolution Challenge, our solution ranks third in both \\(imes \\)2 and \\(imes \\)3 tracks.doi:10.1007/978-3-030-67070-2_27Kaihua ChengChenhuan Wu
现在的大部分方法都通过调整网络结构,使网络具有获得更丰富表示特征的能力,如attention、AutoML、NAS。 本文提出的自校准卷积(Self-Calibrated conv),通过增强每一层卷积的能力,提升整个网络的能力。SC conv将原卷积分为多个不同的部分,由于这种异质的卷积操作和核之间的交流,每个位置的感受野都被扩大了。 优点:每一个...
## Multi-DConv Head Transposed Self-Attention (MDTA) class Attention(nn.Module): def __init__(self, dim, num_heads, bias): super(Attention, self).__init__() self.num_heads = num_heads self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) self.qkv = nn.Conv2d(dim, d...
在2020cvpr上面我又看到一篇挺好的文章,这里分享给大家。这个文章是ImprovingConvolutionalNetworkswithSelf-Calibrated...self-attention的操作,但是相比于自注意力操作,这里大量简化了操作,并没有增大多少参数就可以得到一个新的特征。可以获得更好的感受野。
we design a self-calibrated cross attention (SCCA) block. For efficient patch-based attention, query and support features are firstly split into patches. Then, we design a patch alignment module to align each query patch with its most similar support patch for better cross attention. Specifically...
Self-Attention module is introduced to match or outperform their convolutional counterparts, which allows the feature aggregation to adapt to each channel. Furthermore, to improve the basic convolutional feature transformation process of Convolutional Neural Networks (CNNs), Self-Calibrated convolution is ...
Cascaded Temporal and Spatial Attention Network for solar adaptive optics image restoration CTSAN consists of four modules: optical flow estimation PWC-Net for inter-frame explicit alignment, temporal and spatial attention for dynamic feature fusion... C Zhang,S Wang,Q Chen,... - 《Astronomy & ...
DivP is implemented using multi-head dot-product attention, with each head estimating the corresponding rater annotation. The estimated probability maps are then used to represent the multi-rater confidences. To avoid trivial solutions, we also shuffle the multi-head loss function of DivP to ...
To address these issues, we propose a novel 3D U-Net related brain tumor segmentation model dubbed as self-calibrated attention U-Net (SCAU-Net) in this work, which simultaneously introduces two lightweight modules, i.e., external attention module and self-calibrate...
2020. Split then Refine: Stacked Attentionguided ResUNets for Blind Single Image Visible Watermark Removal. arXiv preprint arXiv:2012.07007 (2020). About [ACM MM 2021] Visible Watermark Removal via Self-calibrated Localization and Background Refinement Topics pytorch watermark watermark-removal acmmm...