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...
PS-YOLO: a small object detector based on efficient convolution and multi-scale feature fusionSmall 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...
(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 ...
为了进一步提高效率,使用depthwise separable convolution进行特征融合,并在每次卷积后使用BN和激活函数。 4. EfficientDet 4.1 EfficientDet Architecture Fig3展示了EfficientDet的整体架构,使用ImageNet预训练的EfficientNet作为backbone,BIFPN作为feature network,takes level 3-7 fetures {P3,P4,P5,P6,P7} from the ...
每次移除一个scale时,我们在两个block中添加一个transition层,该层在通过strided convolution将fine-scale feature输入到coraser scale前利用1x1卷积concatenat特征并且将channel切割为原来的一半,这一步有点像DenseNet-BC。第二,由于l层的分类器仅使用最coarse scale上的特征,l层上较为fine的feature map和先前S-2层...
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...
(Long et al., 2015). Further, we intend to explore approaches that combine MSDNets with model compression (Chen et al., 2015; Han et al., 2015), spatially adaptive computation (Figurnov et al., 2016) and more efficient convolution operations (Chollet, 2016; Howard et al., 2017) to ...