activity
20152020
most citedLearning Gaussian Instance Segmentation in Point Clouds

36 citations · 49 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2020★ 36 cited

Learning Gaussian Instance Segmentation in Point Clouds

Shih-Hung Liu, Shang-Yi Yu, Shao-Chi Wu +2

This paper presents a novel method for instance segmentation of 3D point clouds. The proposed method is called Gaussian Instance Center Network (GICN), which can approximate the di…

cs.CV2019★ 4 cited

Silhouette-Net: 3D Hand Pose Estimation from Silhouettes

Kuo-Wei Lee, Shih-Hung Liu, Hwann-Tzong Chen +1

3D hand pose estimation has received a lot of attention for its wide range of applications and has made great progress owing to the development of deep learning. Existing approache…

cs.CL2016★ 2 cited

Learning to Distill: The Essence Vector Modeling Framework

Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen +1

In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Lea…

cs.CL2016★ 1 cited

Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization

Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen +2

Word embedding methods revolve around learning continuous distributed vector representations of words with neural networks, which can capture semantic and/or syntactic cues, and in…

cs.CL2016★ 2 cited

Improved Spoken Document Summarization with Coverage Modeling Techniques

Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen +1

Extractive summarization aims at selecting a set of indicative sentences from a source document as a summary that can express the major theme of the document. A general consensus o…

cs.CL2015★ 4 cited

Leveraging Word Embeddings for Spoken Document Summarization

Kuan-Yu Chen, Shih-Hung Liu, Hsin-Min Wang +2

Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representa…