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Found 19 papers, 6 papers with code
Generate and Edit Your Own Character in a Canonical View
Although the progress of generative models enables the stylization of a portrait, obtaining the stylized image in canonical view is still a challenging task.
Efficient dynamic filter for robust and low computational feature extraction
However, the performance of the dynamic filter might be degraded since simple feature pooling is used to reduce the computational resource in the IDF part.
Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN
To address this issue, we propose a novel GAN model, i. e., AU-GAN, which has an asymmetric architecture for adverse domain translation.
Action Recognition with Domain Invariant Features of Skeleton Image
Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community.
A Teacher-Student Framework with Fourier Augmentation for COVID-19 Infection Segmentation in CT Images
Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19.
Lightweight dynamic filter for keyword spotting
This lightweight dynamic filter is applied to the front-end of KWS to enhance the separability of the input data.
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment.
Ranked #3 on
Medical Image Segmentation
on ETIS-LARIBPOLYPDB
Memory-based Semantic Segmentation for Off-road Unstructured Natural Environments
In this paper, a built-in memory module for semantic segmentation is proposed to overcome these problems.
CPNet: Cross-Parallel Network for Efficient Anomaly Detection
Anomaly detection in video streams is a challenging problem because of the scarcity of abnormal events and the difficulty of accurately annotating them.
Few-shot Learning for CT Scan based COVID-19 Diagnosis
To compensate for the sparseness of labeled data, the proposed method utilizes a large amount of synthetic COVID-19 CT images and adjusts the networks from the source domain (synthetic data) to the target domain (real data) with a cross-domain training mechanism.
CAFE-GAN: Arbitrary Face Attribute Editing with Complementary Attention Feature
The goal of face attribute editing is altering a facial image according to given target attributes such as hair color, mustache, gender, etc.
Unsupervised domain adaptation based COVID-19 CT infection segmentation network
In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images.
COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network
However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease.
Data Separability for Neural Network Classifiers and the Development of a Separability Index
In machine learning, the performance of a classifier depends on both the classifier model and the dataset.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement
Performance of learning based Automatic Speech Recognition (ASR) is susceptible to noise, especially when it is introduced in the testing data while not presented in the training data.
Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation
Audio waveform generation can then be performed using the proposed network.
KU-ISPL Speaker Recognition Systems under Language mismatch condition for NIST 2016 Speaker Recognition Evaluation
As development dataset which is spoken in Cebuano and Mandarin, we could prepare the evaluation trials through preliminary experiments to compensate the language mismatched condition.
KU-ISPL Language Recognition System for NIST 2015 i-Vector Machine Learning Challenge
The experimental results show that the use of duration and score fusion improves language recognition performance by 5% relative in LRiMLC15 cost.
