今天的这篇文章或许能带给你新视角,微软亚洲研究院的研究员们从Local Attention和Dynamic Depth-wise Convolution的视角出发发现,设计好的卷积结构并不比Transformer差!相关论文“On the Connection between Local Attention and Dynamic Depth-wise Convolution”已被ICLR 2022收录。 论文链接:https://arxiv.org/abs/2106...
Regarding the layer structure, the general layout loosely follows the model presented in Noise2Noise [27], differing mainly in the number of channels at each network block and the downsampling/upsampling layers. Instead of max pooling and 2D upsampling layers, SReD uses 2D convolutions with ...
The secondConvolution layer with strid=2 act like maxpool(2), means it reduce the channel size by 2x2. UseDepthwise Convolutionfor a light-weight model In the world of CNN, while thinking of light-weight model, the first option comes to our mind will be MobileNet. In mobilenet use of ...
but it is slightly different from local attention. Depth-wise convolution shares weights in the spatial dimension, where each position is aggregated with the same weight of the convolution kernel. In the channel dimension, each channel
Transformer 的文章近两年来可谓是井喷式爆发,大量工作来设计各种任务上的 transformer 模型,然而,attention 作为 transformer 的核心模块,真的比卷积强吗?这篇文章为你带来了 local attention 和 dynamic depth-wise convolution 的新视角,其实设计好的卷积结构,并不比 transformer 差!
We first constructed a block by combining the skip connections with the conventional convolution layers based on the depth separable convolution layer (SeparableConv2D layer). Subsequently, this block is combined with the block in ResNet for extracting features containing depth information. When dealing...
Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural NetworksYeonjong Shin
With the sequential inter-query self-attention ('I') and visual cross- attention ('V ') layers, we insert the depth cross-attention layer ('D') into each decoder block with four positions. For 'I → D + V ', we fuse the depth and visual embeddings fD...
6 (b), network with the 2D feature extraction significantly outperforms the single layer one on validation loss. Cost Metric We also compare our variance operation based cost metric with the mean operation based metric [11]. The element-wise variance operation in Eq. 2 is replaced with the ...
Monocular 3D object detection is challenging due to the lack of accurate depth information. Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images. Depth-base