Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Images This repository provides the official PyTorch implementation of CFPT. In this paper, we propose the cross-layer feature pyramid transformer designed for small object detection in aerial images. Below is the performance ...
In this paper, we introduce the Cross-Layer Feature Pyramid Transformer (CFPT), a novel upsampler-free feature pyramid network designed specifically for small object detection in aerial images. CFPT incorporates two meticulously designed attention blocks with linear computational complexity: the Cross-...
Cross-layer Feature Pyramid Network for Salient Object Detectiondoi:10.1109/TIP.2021.3072811Congyan LangJiashi FengJunhao LiewQibin HouYidong LiZun Li
self.projs.append(nn.Conv2d(in_chans, dim, kernel_size=ps, stride=stride, padding=padding)) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints assert H =...
MPSRnnMatrixTrainingLayer MPSRnnMatrixTrainingState MPSRnnRecurrentImageState MPSRnnRecurrentMatrixState MPSRnnSequenceDirection MPSRnnSingleGateDescriptor MPSScaleTransform MPSSize MPSState MPSStateBatch MPSStateResourceList MPSStateResourceType MPSStateTextureInfo MPSTemporaryImage MPSTemporary...
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Although feature pyramid strategy has been used before, they applied the pyramid strategy of part image, attention region, or layer features in traditional person ReID task, while pyramid resolution restoration has not been exploited for cross-resolution ReID scenario yet. In fact, the usage of ...
This feature matrix is then fed into the SPP layer to fix the encoding dimension.(Equation 1)y=Fc(x,{Wi})+x Figure 6C shows the principle of integrating the encoded information into the SPP layer. The input of this module is the feature matrix extracted by the Res-Blocks. This module ...
Our backbone extracts feature maps from the template image and the search region image, which are fed into Encoder layer for feature enhancement. These enhanced features are then passed through the Decoder consisting of cross-correlation and feedforward Implementation details Training Our model is ...
A transformer encoder is composed of a sequence of blocks where each block con- tains multiheaded self-attention (MSA) with a feed-forward network (FFN). FFN contains two-layer multilayer percep- tron with expanding ratio r at the hidden layer, and...