代码:https://github.com/ofsoundof/GRL-Image-Restoration 这个论文的代码地址叫GRL,意思是 Global, Regional, Local 的意思,作者从三个尺度对特征建模,核心是构建了一个 anchored strip self-attention。 如何从Global, Regional, Local三个尺度有效对特征建模,是当前难题。作者首先观察一个现象发, 下图所示,低分...
代码:github.com/ofsoundof/GR 这个论文的代码地址叫GRL,意思是 Global, Regional, Local 的意思,作者从三个尺度对特征建模,核心是构建了一个 anchored strip self-attention。 如何从Global, Regional, Local三个尺度有效对特征建模,是当前难题。作者首先观察一个现象发, 下图所示,低分辨率图像中青色点的 attention...
The attention mechanism has gained significant recognition in the field of computer vision due to its ability to effectively enhance the performance of deep neural networks. However, existing methods often struggle to effectively utilize spatial information or, if they do, they come at the cost of ...
Speaker: Furong Huang Affiliation: The University of Maryland Title: Efficient Machine Learning at the Edge in Parallel Abstract: Since the beginning of the digital age, the size and quantity of data sets have grown exponentially because of the proliferation of data captured by mobile devices, vehi...
例如,Sparse Transformer 将其一半的头部分配给模式,结合strided 和local attention。 类似地,Axial Transformer 在给定高维张量作为输入的情况下,沿着输入张量的单轴应用一系列的self attention计算。 本质上,模式组合以固定模式相同的方式降低了内存的复杂度。 但是,不同之处在于,多模式的聚集和组合改善了self attention...
讲者: Yingbin Liang Professor at the Department of Electrical and Computer Engineering at the Ohio State University (OSU) 讲座题目:Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-...
《Frame Attention Networks for Facial Expression Recognition in Videos》(ICIP 2019) GitHub:O网页链接《Bidirectional Scene Text Recognition with a Single Decoder》(2019) GitHub:O网页链接《Self-training with Noisy Student improves ImageNet classification》(2019) GitHub:O网页链接...
@InProceedings{Arar_2022_CVPR, author = {Arar, Moab and Shamir, Ariel and Bermano, Amit H.}, title = {Learned Queries for Efficient Local Attention}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022} } ...
class LocalWindowAttention(nn.Layer): def __init__(self, dim, key_dim, num_heads=8, attn_ratio=4, resolution=14, window_resolution=7, kernels=[5, 5, 5, 5],): super().__init__() self.dim = dim self.num_heads = num_heads self.resolution = resolution ...
2 Efficient Channel Attention (ECA) Module SE (Global pooling-FC[r]-ReLu-FC-sigmoid),FC[r]就是使用压缩比(降维)为 r 的FC层。 SE-Var1(0参数的SE,Global pooling-sigmoid) SE-Var2 (Global pooling-[·]-sigmoid),[·]为点积操作。