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Found 12 papers, 2 papers with code
RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose
Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency.
Ranked #3 on
Pose Estimation
on OCHuman
(using extra training data)
Hierarchical Memory Pool Based Edge Semi-Supervised Continual Learning Method
In addition, in order to further reduce the computational overhead for unlabeled samples, EdgeHML leverages a progressive learning method.
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge
Upon training for a new task, the gradient integrator ensures the prevention of catastrophic forgetting and mitigation of negative knowledge transfer by effectively combining signature tasks identified from the past local tasks and other clients' current tasks through the global model.
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
Deep neural networks (DNN) are a promising tool in medical applications.
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision
The prior art sheds light on exploring the accuracy-resource tradeoff by scaling the model sizes in accordance to resource dynamics.
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
A Polynomial Roth Theorem for Corners in Finite Fields
We prove a Roth type theorem for polynomial corners in the finite field setting.
Classical Analysis and ODEs Combinatorics Number Theory
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Today's available datasets in the wild, e. g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i. e. erroneous, labels.
A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion
Abstract—Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC).
RAD: On-line Anomaly Detection for Highly Unreliable Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
Facial Makeup Transfer Combining Illumination Transfer
To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software.
