Search Results for author:
Found 6 papers, 1 papers with code
An Emergency Disposal Decision-making Method with Human--Machine Collaboration
The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list.
Prototype as Query for Few Shot Semantic Segmentation
In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.
Ranked #11 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Decision-making of Emergent Incident based on P-MADDPG
In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and makes decisions before the incident occurs, thus solving the problem that the decision speed cannot keep up with the spreading speed.
Zero-sample surface defect detection and classification based on semantic feedback neural network
Defect detection and classification technology has changed from traditional artificial visual inspection to current intelligent automated inspection, but most of the current defect detection methods are training related detection models based on a data-driven approach, taking into account the difficulty of collecting some sample data in the industrial field.
Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN
The generator consists of feature extraction and image reconstruction.
A self-adapting super-resolution structures framework for automatic design of GAN
In this paper, we introduce a new super-resolution image reconstruction generative adversarial network framework, and a Bayesian optimization method used to optimizing the hyperparameters of the generator and discriminator.
