Liang Shuang — accepted AI-conference papers

2024

RoadPainter: Points Are Ideal Navigators for Topology transformER

Zhongxing Ma, Liang Shuang, Yongkun Wen, Weixin Lu, Guowei Wan*

ECCV 2024poster

"Topology reasoning aims to provide a precise understanding of road scenes, enabling autonomous systems to identify safe and efficient routes. In this paper, we present RoadPainter, an innovative approach for detecting and reasoning the topology of lane centerlines using multi-view images. The core…

Cited by 8 Source ↗PDF ↗Scholar ↗

2021

EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture

Chenfeng Miao, Liang Shuang, Zhengchen Liu, Chen Minchuan, Jun Ma, Shaojun Wang +1

ICML 2021spotlight

In this work, we address the Text-to-Speech (TTS) task by proposing a non-autoregressive architecture called EfficientTTS. Unlike the dominant non-autoregressive TTS models, which are trained with the need of external aligners, EfficientTTS optimizes all its parameters with a stable, end-to-end trai…

Cited by 52 Source ↗PDF ↗code ↗Scholar ↗