Additionally, the paper presents the STC module to address the problem of poor feature correlation in high-resolution image processing by CNN networks. This module improves feature expressiveness and range, strengthening inter-feature relationships within layers. Furthermore, in the target positioning ...
Multi-scale Feature Fusion Group 为获得精确的边缘信息,我们构建了CCB模块,见上图。除了Cross卷积外,CCB还包含F-Norm与CA(通道注意力,没什么可说的),两者分别用于空域与通道信息重要性挖掘。F-Norm可表示如下:F(i)out=(F(i)in⊗k(i)+b(i))+F(i)in 更多关于F-Norm的介绍可参考《Iterative Network fo...
Multi-scale Feature Fusion Group 为获得精确的边缘信息,我们构建了CCB模块,见上图。除了Cross卷积外,CCB还包含F-Norm与CA(通道注意力,没什么可说的),两者分别用于空域与通道信息重要性挖掘。F-Norm可表示如下: 更多关于F-Norm的介绍可参考《Iterative Network for Image Super-resolution》一文,为方便理解,笔者在...
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution Ao Li1 , Le Zhang1*, Yun Liu2 , Ce Zhu1 1University of Electronic Science and Technology of China, 2I2R, A*STAR aoli@std...
Fusion] [DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion] [FusionGAN: A generative adversarial network for infrared and visible image fusion] [PIAFusion: A progressive infrared and visible image fusion network based on illumination aw] [CDDFuse...
In this paper, an uncertainty-aware domain adaptive detection method is proposed to bridge the gap of different domain data from different scenes, thereby improving the cross-scene detection performance in high-resolution SAR images. Following the one-stage detection framework, the cross-scene target...
CMDet由三条分支组成,RGB infrared fusion中。Fusion是两个模态的融合分支。两个模态选用的Backbone分别是对应的ResNet 50。然后将对应两个模态的Feature map,concat后利用1*1卷积进行维数约减的操作。由于Baseline是two-stage,检测头RPN+roi transformer对应的损失函数,同Faster-RCNN(好吧我还没看Roi transformer ...
4.2. Feature Point Decoder Each pixel value output by using the feature point decoder corresponds to the probability that the pixel on the input image belongs to the feature point. Feature point detectors with explicit decoders use pixel shuffle to upsample feature maps back to full resolution siz...
DAFormer stabilizes training and avoids overfitting to the source domain via the rare class sampling, thing-class ImageNet feature distance, and learning rate warmup. HRDA (Hoyer et al., 2022b) presents a multi-resolution training approach for the UDA in semantic segmentation, combining high-...
Domain-invariant feature-extraction has become very popular for unsupervised domain adaptation (UDA) person re-identification (Re-ID). However, most methods using it are limited by weak discrimination of learned domain-invariant features. To solve this problem, we develop a new approach: cross-adver...