To solve the problem that the feature information of pole-like obstacles in complex environments is easily lost, resulting in low detection accuracy and low real-time performance, a multi-scale hybrid attention mechanism detection algorithm is proposed in this paper.
The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task.
Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.
In our task, saliency maps are used to assist the identification and visualization of developmental landmarks.
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners.
However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions.
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Ranked #1 on Electron Microscopy Image Segmentation on SNEMI3D
In the simplest case, the proposed multi-stage VAE divides the decoder into two components in which the second component generates refined images based on the course images generated by the first component.