Multiscale feature fusion approach: (a) the gray lines represent the fusion of the P2 feature layer with the detection layer separately, (b) the blue lines represent the fusion of the P2 feature layer with the detection layer successively. Full size image CRA block Residual blocks are a commo...
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...
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion笔记和代码 本篇论文的主要创新点是SOS增强策略和密集特征融合,创新点均是从其他领域进行挖掘。 摘要 提出了一种基于U-Net结构的具有密集特征融合的多尺度增强去雾网络。 该方法基于增强反馈和误差反馈两种原理进行了设计,并证明了该方法适用于脱雾问题。
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach 阿梁 3 人赞同了该文章 论文动机 (1)尽管hierarchical encoder中的transformer块在不同的阶段捕捉全局信息,但transformer块只处理patch merging后的特征层,该特征层使用单一的感受野的大小。因此,在每个阶段中没有适当地利...
When the image information enters the block, it is decomposed into four subbands through DWT, and then it concats the four subbands. The resolution of the obtained feature information becomes half of the input, and the number of channels become 4 times, as a result the number of channels ...
为了解决上述问题,我们设计了一种多尺度扩张残差块(MDRB)fMDRB multi-scale dilated residual block (MDRB),它不仅可以有效地扩大感受野 receptive field 以感知帧之间的大像素运动, 还可以 在扩张卷积的帮助下可以很好地保留对象边界细节 捕获多尺度上下文信息。
The development of the Internet of Things and 3D technology promotes the wide application of face models in 3D animation. However, because the expression is inconsistent with the facial muscle moveme...
The scene classification method of remote sensing images proposed in this paper, which is based on deep networks and multi-scale feature fusion, not only can input rich remote sensing images for the deep networks and increase the number of labeled samples, but also can reduce information loss br...
Intra-stage Feature Fusion (IFF)和轴注意力比较像,沿着H轴和W轴做了pooling Dual Transformer Bridge 就是把四个不同维度的特征拉直concat,然后做完attention后再分开,以此做到跨stage的attention 其中channel aware和token aware如下 实验 ablation study
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。