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Found 12 papers, 7 papers with code
MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environment
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.
Multi-scale Attentive Image De-raining Networks via Neural Architecture Search
The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task.
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
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.
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
In our task, saliency maps are used to assist the identification and visualization of developmental landmarks.
Line Graph Neural Networks for Link Prediction
In this formalism, a link prediction problem is converted to a graph classification task.
Structural Plan of Indoor Scenes with Personalized Preferences
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.
Deep Learning of High-Order Interactions for Protein Interface Prediction
However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions.
Protein Interface Prediction
Vocal Bursts Intensity Prediction
Adversarial Model for Rotated Indoor Scenes Planning
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Ranked #1 on
Electron Microscopy Image Segmentation
on SNEMI3D
Electron Microscopy Image Segmentation
Image Segmentation
+1
Dense Transformer Networks
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
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.
