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...
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...
Small object detectionMulti-scale feature fusionDownsamplingCompared to generalized object detection, research on small object detection has been slow, mainly due to the need to learn appropriate features from limited information about small objects. This is coupled with difficulties such as information ...
(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 ...
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...
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 ...
挑战1:efficient multi-scale feature fusion, 在融合不同的特征时,以往的大多数工作只是简单的sum them up without distinction,然而,这些不同的特征具有不同的分辨率,通常它们对fused output feature的贡献不等。为了解决这个问题,本文提出一种简单但高效地weighted bi-directional feature pyramid network(BiFPN),在rep...
2, the network mainly consists of Multi-scale Modulation Module (3M) stack. In detail, we first apply a 1 × 1 convolution for the transformation of the input image into shallow features. Then, a number of stacked 3Ms are used to generate finer depth features for the reconstruction of the...
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...
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2015). arXiv preprint arXiv:1511.07122 Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,...