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Found 17 papers, 8 papers with code
MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion
We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments.
HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders
This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments.
Context-Preserving Two-Stage Video Domain Translation for Portrait Stylization
Portrait stylization, which translates a real human face image into an artistically stylized image, has attracted considerable interest and many prior works have shown impressive quality in recent years.
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model.
Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting
In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences.
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN
Owing to the disentangled feature space, our method can smoothly control the degree of the source features in a single model.
Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring
To resolve it in transferring, we investigate distillation of structures of representations specified to three types: intra-feature, local inter-feature, global inter-feature structures.
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement
Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly.
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations
For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories.
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth
Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neural networks.
Ranked #26 on
Monocular Depth Estimation
on KITTI Eigen split
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
With the development of 3D scanning technologies, 3D vision tasks have become a popular research area.
Ranked #8 on
3D Point Cloud Linear Classification
on ModelNet40
3D Point Cloud Linear Classification
Point cloud reconstruction
A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits
Inferring correct labels from multiple noisy answers on data, however, has been a challenging problem, since the quality of the answers varies widely across tasks and workers.
Self-supervised GAN Detector
Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news.
MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
When images are recaptured on display screens, various patterns differing by the screens as known as the moir\'e patterns can be also captured in spoof images.
FICGAN: Facial Identity Controllable GAN for De-identification
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility.
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images.
TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection
We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues ($i. e.,$ heatmap) instead of oriented box offsets regression.
