Liang Tang — accepted AI-conference papers

2026

BAMI: Training-Free Bias Mitigation in GUI Grounding

Borui Zhang, Bo Zhang, Bo Wang, Wenzhao Zheng, Yuhao Cheng, Liang Tang +3

CVPR 2026

GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ScreenSpot-Pro benchmark, existing models often suffer from suboptimal performance. Utilizing the proposed Masked Prediction Distribution (MPD) attrib

Cited by 0 Source ↗code ↗Scholar ↗

2018

Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution

Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang

NeurIPS 2018poster

The high-dimensional convolution is widely used in various disciplines but has a serious performance problem due to its high computational complexity. Over the decades, people took a handmade approach to design fast algorithms for the Gaussian convolution. Recently, requirements for various non-Gaus…

Cited by 3 Source ↗PDF ↗Scholar ↗