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 ...
& 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 ...
💻 Visibility-aware Multi-view Stereo Network [paper] [Github] Long-range Attention Network for Multi-View Stereo [paper] CVPR2021 💻 PatchmatchNet: Learned Multi-View Patchmatch Stereo [paper] [Github] ICCV2021 3DV 2021 💻 Deep Multi-View Stereo gone wild. [paper] [Project] [Github]...
Notes: they show thattwo tasks with high affinity at a certain scale are not guaranteed to retain this behaviour at other scales, and vice versa. [AM-CNN] Lyu, K., Li, Y., & Zhang, Z.Attention-aware multi-task convolutional neural networks.TIP, 2020. ...
4, each MHA layer comprises a multi-head self-attention submodule, an MHA submodule, a normalization submodule, and a feed forward network (FFN) submodule. The entire module consists of NL MHA layers. For query qi∈Rn×CH(i=1,2,⋯,H), key ki∈Rn×CH(i=1,2,⋯,H), and value ...
In recent years, multi-sensor fusion system, which integrates IMU, GNSS, and LiDAR data in an optimization or filtering framework, receives more attention because of its robustness and accuracy in complex environments. Compared with loosely-coupled fusion schemes, which may lead to loss of ...
The features collected from the image features of the reference points then interact with each other through a multi-head self-attention layer [9]. After a series of self-attention layers, we read off bounding box parameters from every layer and use a set-to-set loss inspired by DETR [10...
These methods integrate features from different views by concatenating [24] or self attention [7] to improve estimation accuracy 2.3. Unsupervised Gaze Representation Learning Unsupervised representation learning has always been a hot topic in Computer Vision community. Recently,...
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{...
to explore contextual information in latent space using the self-attention mechanism. Owing to the super-patch based data representation and super-patch based transformer structure, we present a multiscale super-patch transformer network (MSSPTNet), which is context-aware and suitable for semantic se...