Shengsen Wu — accepted AI-conference papers

2023

Switchable Representation Learning Framework With Self-Compatibility

Shengsen Wu, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Ling-Yu Duan

CVPR 2023poster

Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resourc…

Cited by 3 Source ↗PDF ↗Scholar ↗

2022

Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation

Shengsen Wu, Liang Chen, Yihang Lou, Yan Bai, Tao Bai, Minghua Deng +1

AAAI 2022technical

In object re-identification (ReID), the development of deep learning techniques often involves model updates and deployment. It is unbearable to re-embedding and re-index with the system suspended when deploying new models. Therefore, backward-compatible representation is proposed to enable ``new''…

Cited by 8 Source ↗PDF ↗Scholar ↗