Search Results for author: Chul Lee

Found 11 papers, 4 papers with code

End-to-end Neural Diarization: From Transformer to Conformer

no code implementations14 Jun 2021 Yi Chieh Liu, Eunjung Han, Chul Lee, Andreas Stolcke

We propose a new end-to-end neural diarization (EEND) system that is based on Conformer, a recently proposed neural architecture that combines convolutional mappings and Transformer to model both local and global dependencies in speech.

Data Augmentation

Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing

1 code implementation ICCV 2021 Jae-Han Lee, Chul Lee, Chang-Su Kim

We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks.

Multi-Task Learning

BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakers

no code implementations5 Nov 2020 Eunjung Han, Chul Lee, Andreas Stolcke

We present a novel online end-to-end neural diarization system, BW-EDA-EEND, that processes data incrementally for a variable number of speakers.

Speaker Diarization

Cross-modal Learning for Multi-modal Video Categorization

no code implementations7 Mar 2020 Palash Goyal, Saurabh Sahu, Shalini Ghosh, Chul Lee

Multi-modal machine learning (ML) models can process data in multiple modalities (e. g., video, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding, activity recognition).

Activity Recognition Object Detection +1

Exploiting Temporal Coherence for Multi-modal Video Categorization

no code implementations7 Feb 2020 Palash Goyal, Saurabh Sahu, Shalini Ghosh, Chul Lee

Multimodal ML models can process data in multiple modalities (e. g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding).

Object Detection Scene Understanding

Semantic Line Detection and Its Applications

no code implementations ICCV 2017 Jun-Tae Lee, Han-Ul Kim, Chul Lee, Chang-Su Kim

Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps.

Classification General Classification +3

A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis

no code implementations8 Dec 2016 Yuelong Li, Chul Lee, Vishal Monga

For HDR video, a stiff practical challenge presents itself in the form of accurate correspondence estimation of objects between video frames.

Image Generation Optical Flow Estimation

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