The MSL module is designed for multi-scale information fusion. It effectively aggregates fine-grained and high-level semantic features from different resolutions, creating rich multi-scale connections between the encoding and decoding processes. On the other hand, the Conv-Attention module incorporates ...
【ARIXV2209】Multi-Scale Attention Network for Single Image Super-Resolution代码:github.com/icandle/MAN 这是来自南开大学的工作,将多尺度机制与大核注意机制结合,用于图像超分辨率。 2022年初,大核卷积火了,Visual Attention Network (VAN)提出将大核卷积划为:depth-wise conv,dilated conv,和 point-wise con...
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...
self.groups = factor# 分组数,默认为32assertchannels // self.groups >0# 确保通道数能够被分组数整除self.softmax = nn.Softmax(-1)# 定义 Softmax 层,用于最后一维度的归一化self.agp = nn.AdaptiveAvgPool2d((1,1))# 自适应平均池化,将特征图缩小为1x1self.pool_h = nn.AdaptiveAvgPool2d((None...
1、Multi-scale Large Kernel Attention (MLKA) MLKA首先使用 Point-wise conv 改变通道数,然后将特征 split 成三组,每个组都使用 VAN 里提出的大核卷积来处理(即depth-wise conv,dilated conv,和 point-wise conv 的组合)。三组分别使用不同尺寸的大核卷积(7×7、21×21、35×35),膨胀率分别设置为(2,...
YOLO series are very classic detection frameworks in the field of object detection, and they have achieved remarkable results on general datasets. Among them, YOLOv5, as a single-stage multi-scale detector, has great advantages in accuracy and speed, but
mlp_hidden_dim=int(dim*mlp_ratio)self.norm1=norm_layer(dim)self.norm2=norm_layer(dim)self.mlp=Mlp(dim,mlp_hidden_dim,act_layer=act_layer,drop=drop)self.attn=Rel_Attention(dim,block_size,num_heads,qkv_bias,qk_scale,attn_drop,drop)self.drop_path=DropPath(drop_path)ifdrop_path>0.els...
we proposed an RGB-D indoor semantic segmentation network based on multi-scale fusion: designed a wavelet transform fusion module to retain contour details, a nonsubsampled contourlet transform to replace the pooling operation, and a multiple pyramid module to aggregate multi-scale information and conte...
EMA(Efficient Multi-Scale Attention)模块是一种新颖的高效多尺度注意力机制,旨在提高计算机视觉任务中的特征表示效果。 EMA注意力模块通过结合通道和空间信息、采用多尺度并行子网络结构以及优化坐标注意力机制,实现了更加高效和有效的特征表示,为计算机视觉任务的性能提升提供了重要的技术支持。 通道和空间注意力的结合:...
Linear attention确实快,但是模型的容量、学习能力是比原始的softmax attention差一些的。 为此,论文引入多尺度tokens(multi-scale tokens)。 具体在下图展示。 图左边是论文提出的EfficientViT Module。由两个模块组成,一个是FFN+DWConv,另一个是Multi-Scale Linear Att。