Search Results for author:
Found 8 papers, 2 papers with code
Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch
When applied to biological data, DiffAug improves performance by up to 10. 1%, with an average improvement of 5. 8%.
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
It is composed of two main modules: open intent detection and open intent discovery.
An Efficient Training Approach for Very Large Scale Face Recognition
This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Non-contact PPG Signal and Heart Rate Estimation with Multi-hierarchical Convolutional Network
Heartbeat rhythm and heart rate (HR) are important physiological parameters of the human body.
Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables
Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.
Community Detection
Physics and Society
General Economics
Economics
Applications
AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition
The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one dataset to another one.
Facial Expression Recognition
Facial Expression Recognition (FER)
A Unified Model for Recommendation with Selective Neighborhood Modeling
To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations.
Visual Object Tracking by Segmentation with Graph Convolutional Network
The proposed model provides a general end-to-end framework which integrates i) label linear prediction, and ii) structure-aware feature information of each superpixel together to obtain object segmentation and further improves the performance of tracking.
