Search Results for author:
Found 10 papers, 4 papers with code
Domain Adaptive Semantic Segmentation by Optimal Transport
Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model.
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image
Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain.
A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields.
Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation
The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks.
Generative Neural Networks for Anomaly Detection in Crowded Scenes
Security surveillance is critical to social harmony and people's peaceful life.
Ranked #3 on
Abnormal Event Detection In Video
on UBI-Fights
Modulated Convolutional Networks
In this paper, we propose new Modulated Convolutional Networks (MCNs) to improve the portability of CNNs via binarized filters.
Memory Attention Networks for Skeleton-based Action Recognition
Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence.
Ranked #89 on
Skeleton Based Action Recognition
on NTU RGB+D
Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition
Gesture recognition is a challenging problem in the field of biometrics.
Ranked #1 on
Hand Gesture Recognition
on MGB
GM-Net: Learning Features with More Efficiency
Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images.
Deep Spatio-temporal Manifold Network for Action Recognition
Visual data such as videos are often sampled from complex manifold.
