Different from previous works directly consider- ing multi-scale feature maps obtained from the inner layers of a primary CNN architecture, we introduce a hierarchical deep model which produces more rich and complementary representations. Furthermore, to refine and robustly fuse the representations ...
通过conv 1x1 -> conv 3x3的方式进行融合,形成N个attention map(N=金字塔层数) 每个ROI Feature与对应的attention map相乘(加权) 将加权后的ROI Feature相加 针对One-stage的适配: 文中指出,这样的理念同样适用于One-stage算法,比如retinanet AugFPN中ROIAlign后面的部分,即Soft ROI Selection在训练中没有用上 Consi...
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。为...
是计算相似性,与 self-attention中类似。 第二部分是得到经相似性相乘得到的feature。 3.实验结果实验细节: 使用了 辅助loss, 测试时使用了multi-scale...。 [Reference] paper:https://arxiv.org/abs/1809.00916 code:https://github.com/PkuRainBow ...
In view of the small size and dense distribution of remote sensing image targets, this paper adds a detection head P2 specifically for small-scale targets on the basis of the three detection layers of the original YOLOv5 model, and involves the shallow high-resolution feature map in the subseq...
To overcome this limitation, in this paper, we propose a novel multi-scale attention (MSA) DNN for accurate object detection with high efficiency. The proposed MSA-DNN method utilizes a novel multi-scale feature fusion module (MSFFM) to construct high-level semantic features. Subsequently, a ...
所以PANet则将proposal对应的整个特征金字塔上的特征提取出来,然后使用全连接网络来提取最大响应的特征。同理,作者就是受到了这个启发,首先对proposal对应的所有层级上的特征进行池化,然后对池化之后的特征进行adaptive feature fusion,即做一个spatial attention得到最后的结果。
The fTAN includes three modules: feature extraction module, Multi-scale Dilated Deformable (MDD) alignment module and attention module. 特征提取模块、多尺度扩张变形(MDD)对齐模块和注意力模块。 1)Feature Extraction Module: 特征提取模块: 由一个卷积层和 5 个带有 ReLU 激活函数的残差块[38] 组成。
Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that resul
解决的问题:transformer全连接的self-attention结构过于复杂,参数多,需要的训练数据多 解决方法:全连接的self-attention改为不同layer不同head各自按不同scale进行连接从而削减参数量。如下图所示,scale反应的是attention计算的时候,两个位置在序列中的距离 (图来自邱博的ppt,侵删) ...