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Found 16 papers, 6 papers with code
Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions
The gearbox is a critical component of electromechanical systems.
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision
With this unpaired mask-text supervision, we propose a new weakly-supervised open-vocabulary segmentation framework (Uni-OVSeg) that leverages confident pairs of mask predictions and entities in text descriptions.
Evolutionary Retrosynthetic Route Planning
The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53. 9%.
Diff-Privacy: Diffusion-based Face Privacy Protection
In this paper, we unify the task of anonymization and visual identity information hiding and propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy.
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head.
An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System
Fault diagnosis is a crucial area of research in industry.
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques
In this paper, a review of methods and techniques for RTSA tasks in non-stationary environments is provided.
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting
Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1, 000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy.
CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Stream
When a new data chunk arrives, we use both real labels and pseudo labels to update the model after prediction and drift detection.
Few-shot Font Generation by Learning Style Difference and Similarity
To address this issue, we propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font).
All-to-key Attention for Arbitrary Style Transfer
In this paper, we propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features -- that is more in line with the characteristics of style transfer.
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap
As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation.
Detection and Isolation of Wheelset Intermittent Over-creeps for Electric Multiple Units Based on a Weighted Moving Average Technique
Different from the existing moving average (MA) technique that puts an equal weight on samples within a time window, WMA uses correlation information to find an optimal weight vector (OWV), so as to better improve the index's robustness and sensitivity.
Learning Discrete Structures for Graph Neural Networks
With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph.
Ranked #3 on
Node Classification
on Cora: fixed 20 node per class
Efficient and Scalable Multi-task Regression on Massive Number of Tasks
Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge.
Robust Continuous Co-Clustering
Clustering consists of grouping together samples giving their similar properties.
