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Found 20 papers, 8 papers with code
A New Learning Paradigm for Stochastic Configuration Network: SCN+
This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN.
Nanorobot queue: Cooperative treatment of cancer based on team member communication and image processing
Although nanorobots have been used as clinical prescriptions for work such as gastroscopy, and even photoacoustic tomography technology has been proposed to control nanorobots to deliver drugs at designated delivery points in real time, and there are cases of eliminating "superbacteria" in blood through nanorobots, most technologies are immature, either with low efficiency or low accuracy, Either it can not be mass produced, so the most effective way to treat cancer diseases at this stage is through chemotherapy and radiotherapy.
Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms
Predicting the COVID-19 outbreak has been studied by many researchers in recent years.
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events
EventZoom is trained in a noise-to-noise fashion where the two ends of the network are unfiltered noisy events, enforcing noise-free event restoration.
RPPLNS: Pay-per-last-N-shares with a Randomised Twist
"Pay-per-last-$N$-shares" (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies.
Fairness
Computer Science and Game Theory
Cryptography and Security
Feature Space Singularity for Out-of-Distribution Detection
Our analysis of the phenomenon reveals why our algorithm works.
Ranked #1 on
Out-of-Distribution Detection
on MS-1M vs. IJB-C
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
Component-wise Adaptive Trimming For Robust Mixture Regression
Existing robust mixture regression methods suffer from outliers as they either conduct parameter estimation in the presence of outliers, or rely on prior knowledge of the level of outlier contamination.
DPGN: Distribution Propagation Graph Network for Few-shot Learning
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #1 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
Learning Delicate Local Representations for Multi-Person Pose Estimation
To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.
Ranked #1 on
Keypoint Detection
on COCO
Learning to Run with Actor-Critic Ensemble
We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e. g., 10-150 MFLOPs).
Ranked #70 on
Person Re-Identification
on DukeMTMC-reID
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on
Scene Text Detection
on COCO-Text
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation.
Effective Quantization Methods for Recurrent Neural Networks
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.
Scene Text Detection via Holistic, Multi-Channel Prediction
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge.
Ranked #6 on
Scene Text Detection
on COCO-Text
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
ICDAR 2015 Text Reading in the Wild Competition
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community.
