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 ...
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...
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...
self.fusion_2 = Decoder_MDCBlock1(16, 4, mode='iter2') self.convd2x = UpsampleConvLayer(32, 16, kernel_size=3, stride=2) self.dense_1 = nn.Sequential( ResidualBlock(16), ResidualBlock(16), ResidualBlock(16) ) self.conv_1 = RDB(8, 4, 8) self.fusion_1 = Decoder_MDCBlo...
The image feature extraction module takes the residual network Resnet10133 as the backbone network for network feature extraction, which mainly consists of four residual network layers (stage1, stage2, stage3, and stage4). Meanwhile, an attention mechanism and a multi-scale feature fusion method...
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 ...
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...
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...
Firstly, the fusion of global and local features is adopted to obtain more information of the vehicle and enhance the learning ability of the model; Secondly, the channel attention module in the feature extraction branch is embedded to extract the personalized features of the targeting vehicle; ...
One of the 3M layers contains a Multi-scale Spatial Feature Module (MSFM) and a Channel Mixing Module (CMM). Finally, we introduce global residual connectivity to learn the detail information and use a 1 × 1 convolution to adjust the channel information. Our network can be defined as, (1...