Can Qin

SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation

2021 · arXiv (Cornell University)

Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed as SLA$^2$P, for unsupervised anomaly detection. …

BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

2025 · arXiv (Cornell University)

Unifying image understanding and generation has gained growing attention in recent research on multimodal models. Although design choices for image understanding have been extensively studied, the optimal model architecture and training recipe for a unified …