Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other. Furthermore, with the help of pixel-wise relative depth values ...
之后,考虑到 Transformer 全局特征提取的出色能力,通过 Swin Transformer 块进一步对这两种模态进行编码。在这个过程中,这两种模式需要共享相同的参数,以便可以从 RGB 模态中学习更多的深度相关信息。在不同的 Swin Transformer 块之间执行下采样操作以提取不同尺度的特征。 在GCM 模块中,RGB 模态的有效指导对于深度补全...
The LocalBins module first predicts Nseed different seed bins at each pixel position at the bottleneck. Each bin is then split into two at every decoder layer using splitter MLPs. The number of bin centers is doubled at every decoder layer and we end up with 2nNse...
• We design a novel local transformer by regarding the extracted features as two sets of point clouds, which is used to exploit 3D geometry information and recon- struct the depth information for each target location. • Experimental results show that our PointDC achieves better or ...
The Swin Transformer [21] uses the idea of the sliding window in convolutional neural networks to reduce the time complexity of the self-attention mechanism to a linear proportion of the image size. Based on this idea, HRFormer [29] applies the Swin Transformer to a high-resolution network ...
There are four major steps to EvolveNet: (1) filter training to strengthen the individuality of layers, (2) depth evolution to find the ideal number of layers, (3) width evolution to compute the ideal width for each layer, and (4) retraining to fine-tune the evolved network. Pre-built...
There are four major steps to EvolveNet: (1) filter training to strengthen the individuality of layers, (2) depth evolution to find the ideal number of layers, (3) width evolution to compute the ideal width for each layer, and (4) retraining to fine-tune the evolved network. Pre-built...
The attention mechanism is able to not only model the relevance between the source and the target, but also generate new representations according to the weights of each component of the source. Technically, the attention model calculates coefficients using a query (Q) and a set of keys (K) ...
Since Swin Transformer V2 has a different patch size for each attention stage, it is easier to extract local and global features from images input by the vision transformer (ViT)-based encoder. Second, to maintain the polymorphism and local inductive bias of the feature map extracted from Swin...
In each Swin Transformer block, images are uniformly partitioned into non-overlapping segments to model contextual information through computing self-attention within local windows. Assuming the resolution of the segmented image is ℎ×𝑤h×w and the number of image patches within each window is ...