Based on the residual learning, we propose a multi-scale feature fusion residual block (MSFFRB) with multiple intertwined paths to adaptively detect and fuse image features at different scales. Furthermore, the outputs of each MSFFRB and the shallow features are used as the hierarchical features ...
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。 具体的是: 首先堆叠两个 3 × 3 和 5 × 5 卷...
The feature pyramid is a classic approach in object detection, and it can exploit multiscale feature information. In previous research, many object detection models that directly use image features extracted by the backbone network were proposed. However, to enable the complementary and fusion of fea...
Residual learningMulti-scale feature fusionFractal networkRecent studies have shown that the use of deep convolutional neural networks (CNNs) can improve the performance of single image super-resolution reconstruction (SISR) methods. However, the existing CNN-based SISR model ignores the multi-scale ...
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。
In multi-class indoor semantic segmentation using RGB-D data, it has been shown that incorporating depth feature into RGB feature is helpful to improve segmentation accuracy. However, previous studies have not fully exploited the potentials of multi-modal feature fusion, e.g., simply concatenating ...
接收所有输出sitch and reshape 原本的resolution,不同的头拼接通过residual block得到输出 Cross Modality Fusion 离散余弦变换变换到频域得到dct特征,借鉴(thinking in frequency)方式,手工的低中高filter得到分解的频率分量 之后反变换为rgb域,最后拼接通道得到频域空间图B (H,W,3)(这里感觉是变为灰度图,灰度图做分...
residual connections to extract high-dimensional feature information. The Position-wise Attention Block is used to capture the spatial dependencies of feature maps, and the Multi-scale Fusion Attention Block is to aggregate the channel dependencies between any feature maps via fusing High and Low-...
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...
mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention...