Search Results for author: Tao Tu

Found 16 papers, 4 papers with code

Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement

1 code implementation18 Jul 2023 Zhixiang Wei, Lin Chen, Tao Tu, Huaian Chen, Pengyang Ling, Yi Jin

2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions.

Disentanglement Segmentation +1

Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement

1 code implementation ICCV 2023 Zhixiang Wei, Lin Chen, Tao Tu, Pengyang Ling, Huaian Chen, Yi Jin

2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions.

Disentanglement Segmentation +1

A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI

1 code implementation NeurIPS 2019 Tao Tu, John Paisley, Stefan Haufe, Paul Sajda

In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data.

EEG Electroencephalogram (EEG)

End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning

no code implementations13 Apr 2019 Tao Tu, Yuan-Jui Chen, Cheng-chieh Yeh, Hung-Yi Lee

In this paper, we aim to build TTS systems for such low-resource (target) languages where only very limited paired data are available.

Cross-Lingual Transfer Transfer Learning

Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning

no code implementations28 Oct 2019 Alexander H. Liu, Tao Tu, Hung-Yi Lee, Lin-shan Lee

In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very close to phoneme sequences of speech utterances.

Clustering Quantization +4

Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation

no code implementations16 May 2020 Tao Tu, Yuan-Jui Chen, Alexander H. Liu, Hung-Yi Lee

The experiment results demonstrate that with only an hour of paired speech data, no matter the paired data is from multiple speakers or a single speaker, the proposed model can generate intelligible speech in different voices.

Speech Synthesis Text-To-Speech Synthesis

Inferring latent neural sources via deep transcoding of simultaneously acquired EEG and fMRI

no code implementations27 Nov 2022 Xueqing Liu, Tao Tu, Paul Sajda

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution.

EEG Electroencephalogram (EEG)

ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

no code implementations ICCV 2023 Tao Tu, Shun-Po Chuang, Yu-Lun Liu, Cheng Sun, Ke Zhang, Donna Roy, Cheng-Hao Kuo, Min Sun

The results demonstrate that ImGeoNet outperforms the current state-of-the-art multi-view image-based method, ImVoxelNet, on all three datasets in terms of detection accuracy.

3D Object Detection object-detection

Towards Accurate Differential Diagnosis with Large Language Models

no code implementations30 Nov 2023 Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu, Amy Wang, Jake Garrison, Karan Singhal, Yash Sharma, Shekoofeh Azizi, Kavita Kulkarni, Le Hou, Yong Cheng, Yun Liu, S Sara Mahdavi, Sushant Prakash, Anupam Pathak, Christopher Semturs, Shwetak Patel, Dale R Webster, Ewa Dominowska, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S Corrado, Yossi Matias, Jake Sunshine, Alan Karthikesalingam, Vivek Natarajan

Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51. 7%) compared to clinicians without its assistance (36. 1%) (McNemar's Test: 45. 7, p < 0. 01) and clinicians with search (44. 4%) (4. 75, p = 0. 03).

DreaMo: Articulated 3D Reconstruction From A Single Casual Video

no code implementations5 Dec 2023 Tao Tu, Ming-Feng Li, Chieh Hubert Lin, Yen-Chi Cheng, Min Sun, Ming-Hsuan Yang

In this work, we study articulated 3D shape reconstruction from a single and casually captured internet video, where the subject's view coverage is incomplete.

3D Reconstruction 3D Shape Reconstruction

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