Search Results for author: Sungmin Lee

Found 7 papers, 0 papers with code

The Spatial Selective Auditory Attention of Cochlear Implant Users in Different Conversational Sound Levels

no code implementations3 Mar 2021 Sara Akbarzadeh, Sungmin Lee, Chin-Tuan Tan

This study attempted to investigate the effect of conversational sound levels on the mechanisms adopted by CI and NH listeners in selective auditory attention and how it affects their daily conversation.

EEG Electroencephalogram (EEG) +2

The effect of speech and noise levels on the quality perceived by cochlear implant and normal hearing listeners

no code implementations3 Mar 2021 Sara Akbarzadeh, Sungmin Lee, Fei Chen, Chin-Tuan Tan

Noise reduction (NR) algorithms used in CI reduce the noise in favor of signal-to-noise ratio (SNR), regardless of plausible accompanying distortions that may degrade the speech quality perception.

Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network

no code implementations24 Apr 2020 Naeem Ul Islam, Sungmin Lee, Jaebyung Park

In contrast, the attribute dependent approaches are effective as these approaches are capable of modifying the required features along with preserving the information in the given image.

Attribute Generative Adversarial Network

Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation

no code implementations ICCV 2019 Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon

We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image.

Object Optical Flow Estimation +2

FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference

no code implementations CVPR 2019 Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon

The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations.

Image Classification Image Segmentation +1

Mutual Suppression Network for Video Prediction using Disentangled Features

no code implementations13 Apr 2018 Jungbeom Lee, Jangho Lee, Sungmin Lee, Sungroh Yoon

Video prediction can be performed by finding features in recent frames, and using them to generate approximations to upcoming frames.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Optical Flow Estimation Representation Learning +1

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