Please note the algorithm used in the Google Maps’ implementation contains a bug that his been fixed in this repository. Usage // Use Google Maps’ point class or any point class with x() and y() methods defined var points = []; var hullPoints = []; var hullPoints_size; // Add ...
For PeleeNet 34, the memory consumption after fusion of CSPNet is reduced by 75% 30. GhostNet 31 is a new lightweight neural network proposed by HAN et al. GhostNet proposes a Ghost module that replaces the traditional convolutional layer. It generates "ghost" feature maps that can extract ...
Hummingbird Update,September 2013: With the Hummingbird update, Google moved away from matching all words in a query to words on a page. It began to ignore words that weren’t hugely relevant to the searcher’s meaning. This meant Google could better deal with natural language (conversational...
O. Predicting excited states from ground state wavefunction by supervised quantum machine learning. Mach. Learn. 1, 045027 (2020). Google Scholar Moreno, JavierRobledo, Carleo, G. & Georges, A. Deep learning the hohenberg-kohn maps of density functional theory. Phys. Rev. Lett. 125, ...
Again, and as in the previous experiments, although MGSA converges more quickly than MCGSA, the latter is capable of avoiding local minima due to the chaotic maps introduced by the G constant, showing the best performance of all the algorithms tested. Fig. 14 Iris problem. Convergence curves...
traffic jams where we were continuously surrounded by moving vehicles. The system performed well and showed a great robustness, without any failure in various extreme scenarios, and demonstrated a high level of precision. The point-cloud map functioned well after being registered on Google Maps. ...
In this solution, pooling is applied after each convolutional block to reduce the spatial dimension of the feature maps [8]. All these methods have been trained using the configuration described in the original paper on the 4000 images from the Five-K dataset. The trained models were then ...
(inter-frame) correlations. Motion estimation and compensation are used to remove the data redundancy and the unimportant visual details. It is apparent that the changes in a scene are due to object motion. Even minute movement can still lead to a great difference between successive frames, ...
There are many module layers in C3D, and the feature maps formed by each module layer after feature extraction are of the same size and densely connected between layers26. The shallow network mainly focuses on texture features, while the deep network focuses on ontology features, and the ...
Noisy segmentation maps were first generated by pixel grouping method based on motions in the videos, and these noisy segmentation maps were then used to train a new segmentation model. The trained networks can improve the quality of the segmentation from the original noisy segmentation and also ...