This paper proposes a lightweight multi-scale feature pyramid structure, which extracts features from network layers of different scales and aggregates them to supplement spatial detail information. Meanwhile, this paper adopts a pair of complementary attention modules, which pay attention to the ...
两种进一步创新的结构:KP-Pyramid 、RandLA-Pyramid Conclusion Abstract 背景 点云分割的最新进展主要是由局部聚合算子和点采样方法的新设计推动的。而与图像分割不同,很少有研究去理解尺度的基本问题以及尺度如何相互作用和融合。 创新 (1)论文研究了如何在点云分割网络中高效且有效地集成不同规模和不同阶段的特征。
Multi-scale text detectionGrouped pyramid moduleEfficient and effectiveScene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a ...
Title: PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer 作者:Duo Li, Anbang Yao, and Qifeng Chen 发表单位:The Hong Kong University of Science and Technology;Intel Labs China 发表于:ECCV 2020 关键词:卷积核,多尺度 一句话总结:在卷积核内部设计多尺度信息提取。对...
我们发现第一种方法效果最好。如表3所示,反褶积层在大多数情况下都有帮助。较小的输入图像的增益更大,这往往有较小的目标。注意,feature map近似增加了简单的计算,没有参数。 上下文嵌入:表3显示了在编码上下文方面的收益。然而,模型参数的数量几乎翻了一番。降维卷积层在不影响精度和速度的前提下,显著降低了...
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
Finally, it generates the feature pyramid by the fused feature map to achieve multi-scale object detection. In addition, we design a default box matching concession method which enables to train the real targets, and increases the number of positive samples. The experiments show that FFDN has ...
the concept of a feature pyramid was introduced. Methods such as FPN24, PANet25, two-way FPN26, etc. enhance model performance by fusing features of different scales through top-down or bottom-up pathways. EfficientDet49introduces a repeatable BiFPN for iterative feature fusion, further enhancing...
浅层可以在高空间分辨率下用小的channel维度建立简单的low-level feature,而深层则可以用更大的channel维度建立更high-level的语义信息,这个是特征金字塔的思想。 Multi Head Pooling Attention:相比于MHA加入了pooling操作,主要作用是改变token个数。其中cls token没有参与pooling操作。
作者提了Multi-Scale and Multi-Stream Deep Feature Learning,可以参考,但是感觉不全。 2. Method 主要创新点: 1)设计了Omni-Scale Residual Block,本质就是加权版多尺度特征融合,Inception升级版。 2)使用了Depthwise Separable Convolutions,成了lightweight network。网络...