Yan Tang — accepted AI-conference papers

2026

LiteSense: Lifting Lightweight ToF with RGB for High-Resolution Metric Depth Estimation

Yusheng Li, Lizhi Lou, Yan Tang, Zekai Miao, Shaoming Zhang, Jianmei Wang

CVPR 2026

Metric depth estimation aims to recover depth maps with absolute scale, high resolution, and cross-scene consistency from visual observations. Existing approaches either rely on large-scale models or costly sensors to preserve metric accuracy and generalization, both ill-suited to resource-constrain

Cited by 0 Source ↗Scholar ↗

2019

Background Adaptation for Improved Listening Experience in Broadcasting

Yan Tang, Trevor J. Cox, Bruno M. Fazenda, Qingju Liu, Wenwu Wang

ICASSP 2019accepted

The intelligibility of speech in noise can be improved by modifying the speech. But with object-based audio, there is the possibility of altering the background sound while leaving the speech unaltered. This may prove a less intrusive approach, affording good speech intelligibility without overly co…

Cited by 2 Source ↗Scholar ↗