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Found 18 papers, 2 papers with code
PG-Net: Pixel to Global Matching Network for Visual Tracking
In this paper, a Pixel to Global Matching Network (PG-Net) is proposed to suppress the influence of background in search image while achieving state-of-the-art tracking performance.
An Artificial Intelligence (AI) workflow for catalyst design and optimization
In this article, we demonstrate the application of this AI workflow in the optimization of catalyst synthesis for ammonia production.
Detecting subtle macroscopic changes in a finite temperature classical scalar field with machine learning
Our finding suggests that the AI method outperforms both the statistical method and the physics method in its sensitivity.
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Sleep stage classification is crucial for detecting patients' health conditions.
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data.
Few Shot Rationale Generation using Self-Training with Dual Teachers
Self-rationalizing models that also generate a free-text explanation for their predicted labels are an important tool to build trustworthy AI applications.
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.
Adaptive Graph Convolutional Subspace Clustering
Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications.
Real-Time Acoustic Perception for Automotive Applications
On the algorithmic side, the I-SPOT Project aims to enable detecting, localizing and tracking environmental audio signals by jointly developing microphone array processing and deep learning techniques that specifically target automotive applications.
RWSC-Fusion: Region-Wise Style-Controlled Fusion Network for the Prohibited X-Ray Security Image Synthesis
Automatic prohibited item detection in security inspection X-ray images is necessary for transportation. The abundance and diversity of the X-ray security images with prohibited item, termed as prohibited X-ray security images, are essential for training the detection model.
MASNet:Improve Performance of Siamese Networks with Mutual-attention for Remote Sensing Change Detection Tasks
In this work we present Mutual-Attention Siamese Network (MASNet), a general siamese network with mutual-attention plug-in, so to exchange information between the two feature extraction branches.
RPT++: Customized Feature Representation for Siamese Visual Tracking
Polar pooling plays the role of enriching information collected from the semantic keypoints for stronger classification, while extreme pooling facilitates explicit visual patterns of the object boundary for accurate target state estimation.
Decouple and Reconstruct: Mining Discriminative Features for Cross-domain Object Detection
In recent years, a great progress has been witnessed for cross-domain object detection.
DCCRGAN: Deep Complex Convolution Recurrent Generator Adversarial Network for Speech Enhancement
Generative adversarial network (GAN) still exists some problems in dealing with speech enhancement (SE) task.
Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection
Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy.
RPT: Learning Point Set Representation for Siamese Visual Tracking
While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem.
Ranked #13 on
Semi-Supervised Video Object Segmentation
on VOT2020
A Closer Look at Disentangling in $β$-VAE
In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others.
Neural Generative Question Answering
Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base.
