Pengfei Shi — accepted AI-conference papers

2024

Estimating Conditional Average Treatment Effects via Sufficient Representation Learning

Pengfei Shi, Wei Zhong, Xinyu Zhang, Ningtao Wang, Xing Fu, Weiqiang Wang +1

IJCAI 2024poster

Estimating the conditional average treatment effects (CATE) is very important in causal inference and has a wide range of applications across many fields. In the estimation process of CATE, the unconfoundedness assumption is typically required to ensure the identifiability of the regression problems…

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2021

R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth Estimating

Zhongkai Zhou, Xinnan Fan, Pengfei Shi, Yuanxue Xin

ICCV 2021poster

In this paper, we propose Recurrent Multi-Scale Feature Modulation (R-MSFM), a new deep network architecture for self-supervised monocular depth estimation. R-MSFM extracts per-pixel features, builds a multi-scale feature modulation module, and iteratively updates an inverse depth through a paramete…

Cited by 145 PDF ↗code ↗Scholar ↗