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...
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...
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 ...
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-...
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...
具体方向包括Sparse Attention Patterns(解决超长的文本比较有效,local attention、block-wise attention)、Memory Saving Designs(reduce dimension、multi-query attention等,multi-query attention在不同head内共享keys和values)、Adaptive Attention(为每个token在每个head上自适应的学习更稀疏有效的attention而非full attention...
@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。
但是这些指标不能反映实际的设备延迟。为了清楚地了解哪些操作和设计选择会减慢边缘设备上 VIT 的推断,在下图中作者作者对不同模型在端侧运行进行了一些分析,主要是分为 ViT 对图像进行分块的 Patch Embedding、Transformer 中的 Attention 和 MLP,另外还有 LeViT 提出的 Reshape 和一些激活等。提出了下面几个猜想。