以下是multi-scale feature fusion的计算公式: F =Σ(Wi * Gi) 其中,F表示融合后的特征向量,Wi表示第i个尺度上特征向量的权重系数,Gi表示第i个尺度上提取的特征向量。权重系数可以根据具体情况进行调整,通常采用softmax函数进行归一化处理,以保证各尺度特征向量的权重之和为1。 在计算过程中,首先从不同尺度的...
Ablation experiments were conducted to evaluate the impacts of MSF and CRA block and investigate the performance of the YOLOv5s model on the NEU-DET and GC10-DET datasets. Table4displays the results of the ablation experiments. To explore the impact of MSF, it can be seen that various feat...
因此,嵌入的特征映射是从多个路径生成的,以在每个patch merging阶段对多尺度表示进行建模。 2) Multi Branch (MB) Transformer Block: 为了保留局部语义表示,除了三个factorized attention以外还包括一个额外的卷积核。factorized attention和 efficient attention 的区别在于,efficient attention 计算 Q 和 K 的 softmax,...
Further, the feature fusion block integrates both global contextual information and spatial features to encode the visual information of an input image. Moreover, spatial-semantic attention module is incorporated to learn attentive contextual features to guide the captioning module. The efficacy of the ...
Cross-Attention Fusion 将CLS 当成是一个分支的抽象信息,那么只需要交换两个分支的 CLS,然后送入 Transformer 中,两个分支的信息就可以进行交互了,这样有助于在另一个分支中引入不同尺度的信息image-20230614214151778上图为实例,就是使用一个 Transformer block 来生成新的 CLS。例子是 Large Branch 的。
In addition, we introduce an innovative multi-scale fusion block by constructing hierarchical residual-like connections within one single residual block, which is great importance for effectively linking the local blood vessel fragments together. Furthermore, we construct a new dataset containing 40 thin...
In the second residual block, the number of channels of feature\(Feature2\)is changed to 96 after the\(3\times 3\)Conv operation, the purpose of this process is to complete the multi-scale aggregation operation later, and also to avoid the distortion of the data caused by excessive compre...
S-Branch:patch 更小,拥有更少的 Encoder block 以及更少的 embedding dimension 这样设计应该是想要平衡性能以及计算开销 两个分支将会混合 L 次,并且最后两个输出都会用于预测。并且两个分支都使用了自学习的位置嵌入 Multi-Scale Feature Fusion 为了让两个分支的数据可以进行融合交互,提出了多种方案 ...
论文阅读《Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection》 摘要 无人机(UAV)图像的目标检测是近年来研究的热点。现有的目标检测方法在一般场景上取得了很好的结果,但无人机图像存在固有的挑战。无人机图像的检测精度受到复杂背景、显著尺度差异和密集排列的小物体的限制。
from networks.base_networks import Encoder_MDCBlock1, Decoder_MDCBlock1 def make_model(args, parent=False): return Net() class make_dense(nn.Module): def __init__(self, nChannels, growthRate, kernel_size=3): super(make_dense, self).__init__() ...