They all tailored the self-attention mechanism to align with the unique properties of point cloud data, enhancing the Transformer’s suitability for point cloud tasks. Yu et al.42 developed PointTr, utilizing th
3, each encoding layer consists of a Multi- head Self-Attention (MSA), a feed-forward network (FFN), residual connections, and layer normalization. At each de- coding layer, after the MSA block, the embeddings enter the Multi-head Cross-Attention (MCA) with the corresponding enc...
& Luo, T. Tsnet: Three-stream self-attention network for rgb-d indoor semantic segmentation. IEEE Intell. Syst. 36, 73–78 (2020). Article CAS Google Scholar Seichter, D., Fischedick, S. B., Köhler, M. & Groß, H.-M. Efficient multi-task rgb-d scene analysis for indoor ...
PAG-Unet incorporates a Pixel-Attention-Guided Fusion module (PAG Fusion) and a Multi-Task Self-Attention module (MTSA) to enhance task-specific feature extraction and reduce task interference. PAG Fusion leverages the relationship between shallow and deep features by using task-specific deep ...
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation.[Paper][Code] Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation.[Paper][Code] ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting....
(2018) proposed a self-cascaded network that uses dilated convolutions (Yu & Koltun, 2016) for multi-scale representation on the last layer of the encoder. Besides including a larger scope of contextual information, multi-scale representation integrates hierarchical dependencies of the context. A ...
It leverages an efficient multi-scale fusion Transformer network based on RGB-D to directly regress the object’s pose. FormerPose can efficiently extract the color and geometric features of objects at different scales, and fuse them based on self-attention and dense fusion method, making it ...
Junhu F, Lin S, Zhou P, Guo Y, Wang Y (2022) M3resu-net: a deep residual network for multi-center colorectal polyp seg- mentation based on multi-scale learning and attention mecha- nism. Phys Med Biol 67(20):205005. https://doi.org/10.1088/ 1361-6560/ac92bb 41. Salpea N, T...
The architecture of the atomic-wise distance-aware self-attention module is shown in Supplementary Fig.S2b. When the input atomic features are denoted as\({x}_{i}\,\), its computation process can be represented as: $${A}_{{{\rm{ij}}}={{Sof\!tmax}}_{{{\rm{j}}}\left(\frac{...
Pocket-aware three-dimensional molecular generation is challenging due to the need to impose physical equivariance and to evaluate protein–ligand interactions when incrementally growing partially built molecules. Inspired by multiscale modelling in condensed matter and statistical physics, we present a ...