Meilin Liu — accepted AI-conference papers

2026

Attack for Defense: Adversarial Agents for Point Prompt Optimization Empowering Segment Anything Model

Xueyu Liu, Xiaoyi Zhang, Meilin Liu, Guangze Shi, Jia Shen, Yujie Wang +5

CVPR 2026

Prompt quality plays a critical role in the performance of the Segment Anything Model (SAM), yet existing approaches often rely on heuristic or manually crafted prompts, limiting scalability and generalization. In this paper, we propose Point Prompt Defender, an adversarial reinforcement learning fr

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2026

OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging

Meilin Liu, Jiaying Wang, Jing Shan

CVPR 2026

Federated learning (FL) has become a promising paradigm for collaborative medical image analysis, yet existing frameworks remain tightly coupled to task-specific backbones and are fragile under heterogeneous imaging modalities. Such constraints hinder real-world deployment, where institutions vary w

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