To enhance the network's ability to focus more on non-discriminative parts of the object and generate high-quality pseudo-masks, the Scale-aware Attention Network (SAN) is proposed. Specifically, a pyramidal attention module is introduced to propagate discriminative information to adjacent object ...
MSA-Net Multi-Scale Attention Network for Crowd Counting 2019 作者:亚马逊 论文:https://arxiv.org/abs/1901.06026 创新点: 在backbone中就产生了多尺度的density map,经过上采样后,加入软注意力机制进行加权叠加。 提出了一个scale-aware loss,但是实验结果好像表明效果不大。... ...
采用了VGG16的backbone,比较三种contribution的效果,发现采用Multi density map+Mask-attention即(+M),以及Img Res(resize到1080P),带来的效果最明显,加了scale-aware loss效果不明显。
First, we propose a scale-aware dynamic convolutional (SAD-Conv) layer for the feature learning of multiple SR tasks with various scales. The SAD-Conv layer can adaptively adjust the attention weights of multiple convolution kernels based on the scale factor, which enhances the expressive power ...
多头混合卷积Multi-head Mixed Convolution(MHMC)和尺度感知聚合Scale-aware Aggregation(SAA) 此外,文中还提出了一种进化混合网络Evolutionary Hybrid Network(EHN),为CNN和transformer的混合网络。 这篇文章提出了两个主要动机: 一个动机是,构建Vision Transformer(ViT)框架时,使用的Self-attention(SA)具有 O(N2) 方...
classifying objects of different sizes. And the entire network is based on the attention mechanism, following the attention setting of SSAN (Spectral-Spatial Attention Networks for Hyperspectral Image Classification), which makes the network pay more attention to pixels in key bands and key spaces. ...
最后,在完成了SAM的设计后,即可搭建本文的视觉Backbone:Scale-Aware Modulation Transformer(SMT),如图11所示。整体上SMT的架构仍旧遵循了(就当前而言)对下游任务友好的层次化结构。 为了提升综合性能,作者团队还提出了所谓的“Evolutionary Hybrid Network”(EHN)的概念,其核心思想就是混合堆叠SAM和MSA(Multi-head Self...
具体来说,在每 K 个骨干块之后执行尺度感知特征自适应(scale-aware feature adaption),如图 3(b)所示。在骨干模块之后,一个尺度感知上采样层(scale-aware upsampling layer)用于任意尺度上采样。 Scale-Aware Feature Adaption 在图三(b)中,特征图F先通过漏斗形模块,以及一个sigmoid,生成一个引导图M.M的值在0...
In this letter, we introduce a real-time and scale-aware 3D Pedestrian Detection, which incorporates a robust encoder network designed for effective pillar feature extraction. The Proposed TriFocus Attention module (TriFA), which integrates external attention and similar attention strategies based on ...
(DCB)module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each branch.Subsequently,the attention operation-based(AOB)module performs attention operations at both branch and channel levels to enhance high-frequency features and ...