Shihao Shao — accepted AI-conference papers

2025

FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields

Shihao Shao, Haoran Geng, Zun Wang, Qinghua Cui

ICLR 2025poster

Machine Learning Force Fields (MLFFs) are of great importance for chemistry, physics, materials science, and many other related fields. The Clebsch–Gordan transform (CG transform) effectively encodes many-body interactions and is thus an important building block for many models of MLFFs. However, th…

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2023

Global Features are All You Need for Image Retrieval and Reranking

Shihao Shao, Kaifeng Chen, Arjun Karpur, Qinghua Cui, André Araujo, Bingyi Cao

ICCV 2023poster

Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking. However, the scalability of this method is often limited due to the significant storage and computation cost incurred by local feature matching in the r…

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