Pixel-shuffle patching preserves the spatial relations in contrast to the standard image patching.Pixel-shuffling is more efficient than the image patching in image classification.The proposed SA-ConvMixer can efficiently learn the segmentation and depth estimation tasks....
It has achieved state-of-the-art results across a wide range of prediction tasks (Chen and Guestrin 2016). A short description, as well as a comparison of the results of the other ML methods, can be found in Sect. A.1 in the appendix. Additionally, two widely used methods for spatial...
, bottom), whereas very dense or sparse networks show a preference for either goal or choices information. As such, the density and related modular small-world structure influences the time horizon of information flowing through the network. Dense networks show greater focus on past information, ...
from these shifted locations. This deformable attention can capture the most informative regions in the image. On this basis, we presentDeformable Attention Transformer (DAT)andDAT++, a general backbone model with deformable attention for both image classification and other dense prediction tasks. ...
Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. represent...
In the same way as image understanding involves the solving of multiple differ- ent tasks, we desire the control over the generation process to include multiple input labels of various kinds. To this end, we design a neural network architecture that is capa- ble of handling sparse and ...
Both of these tasks are computationally intensive. 4.2.1. Large data It is at this point that the computational burden of constructing ΣY and computing its determinant as well as its inverse becomes apparent. Recall that W is a matrix of dimension n×(np) and Ση is of dimension (np)...
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems, pages 13–23, 2019. [30] Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jonathon Shlens. Stand-alone self-...
Deep neural networks have recently shown remarkable performance in classification tasks (Angermueller et al., 2016). The models can efficiently deal with high-dimensional data and learn sufficiently complex, nonlinear functions in a data-driven manner to map data points to their respective classes. ...
Ordinary Markov chains describe successive states of a system. For instance, the successive states could be used to describe the time succession of the number of tasks queued for computer processing. Markov chains can also describe successive states of a system in space rather than in time. The...