no code implementations • 4 May 2024 • Siyuan Yan, Cheng Luo, Zhen Yu, ZongYuan Ge
To address this, we propose a plug-and-play feature augmentation method called LDFS (Language-Guided Diverse Feature Synthesis) to synthesize new domain features and improve existing CLIP fine-tuning strategies.
2 code implementations • 5 Jan 2024 • Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
1 code implementation • 4 Apr 2023 • Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatrainst, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset.
1 code implementation • 2 Mar 2023 • Siyuan Yan, Jing Zhang, Nick Barnes
To effectively model the two types of uncertainty, we introduce a Bayesian generative model to simultaneously estimate the posterior distribution of model parameters and its predictions.
no code implementations • CVPR 2023 • Siyuan Yan, Zhen Yu, Xuelin Zhang, Dwarikanath Mahapatra, Shekhar S. Chandra, Monika Janda, Peter Soyer, ZongYuan Ge
We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen.
no code implementations • 12 Aug 2019 • Dawei Li, Yan Cao, Guoliang Shi, Xin Cai, Yang Chen, Sifan Wang, Siyuan Yan
The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.
no code implementations • 2 Jul 2019 • Dawei Li, Siyuan Yan, Xin Cai, Yan Cao, Sifan Wang
In this paper, we present an integrated filter which comprises a weighted local guided image filter and a weighted spatiotemporal tree filter.