classDetectionPredictor(BasePredictor):defpostprocess(self,preds,img,orig_imgs):preds=ops.non_max_suppression(preds,self.args.conf,self.args.iou,agnostic=self.args.agnostic_nms,max_det=self.args.max_det,classes=self.args.classes)ifnotisinstance(orig_imgs,list):orig_imgs=ops.convert_torch2numpy...
EMCAD leverages a unique multi-scale depth-wise convolution block, significantly enhancing feature maps through multi-scale convolutions. EMCAD also employs channel, spatial, and grouped (large-kernel) gated attention mechanisms, which are highly effective at capturing intricate spatial relationships while...
ConvolutionImage reconstructionImage resolutionTrainingTask analysisKernelConvolutional neural network has shown its superior performance in single image super resolution(SR) as in other computer vision applications. One of its disadvantage is that the computation needs will be significantly higher than that ...
of different input features, while repeatedly applying topdown and bottom-up multi-scale feature fusion. Challenge 2: model scaling – While previous worksmainly rely on bigger backbone networks [17, 27, 26, 5] orlarger input image sizes [8, 37] for higher accuracy, we observe that scaling...
(2015) with 3 layers of 2D convolutions. That method performed less well on this challenging task (Maier et al., 2017). This points out the advantage offered by 3D context, the large field of view of DeepMedic thanks to multi-scale processing and the representational power of deeper ...
解决:Multi-scale features maps 让所有的分类器仅使用coarse-level features,在特定层的feature map 通过concatenate一个或两个卷积来进行计算,包括两种情况:一是对于将常规卷积应用于前一层的相同scale特征上的结果(Figure2中水平连接)二是对于前一层对fine-sale的特征图应用跨步卷积的结果(Figure2中对角线连接)。水...
How to perform multi-scale context aggregation within limited computation budget is important. In this paper, firstly, we introduce a novel and efficient module called Cascaded Factorized Atrous Spatial Pyramid Pooling (CF-ASPP). It is a lightweight cascaded structure for Convolutional Neural Networks...
connection to improve feature reuse and use efficient convolutions for improving efficiency. In DenseDsc, the efficient depthwise separable convolution is used to improve the efficiency. In Dense2Net, we use group convolution to improve the parameter efficiency. And the multi-levels group convolutions...
multi-scale filters. For comparison, the traditional convolutional layers and fully connected layer used in classic CNNs were kept in SeismicPatchNet to show the advantages of the newly designed topological fusion modules. Only some regular operations like traditional convolution, activation, and ...
本文给大家带来的改进机制是EfficientViT(高效的视觉变换网络),EfficientViT的核心是一种轻量级的多尺度线性注意力模块,能够在只使用硬件高效操作的情况下实现全局感受野和多尺度学习。本文带来是2023年的最新版本的EfficientViT网络结构,论文题目是'EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Pred...