4 papers
Bounded Ratio Reinforcement Learning
Yunke Ao, Le Chen, Bruce D. Lee +5
Proximal Policy Optimization (PPO) has become the predominant algorithm for on-policy reinforcement learning due to its scalability and empirical robustness across domains. However…
Time-Contrastive Pretraining for In-Context Image and Video Segmentation
Assefa Wahd, Jacob Jaremko, Abhilash Hareendranathan
In-context learning (ICL) enables generalization to new tasks with minimal labeled data. However, mainstream ICL approaches rely on a gridding strategy, which lacks the flexibility…
Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts
Assefa Seyoum Wahd, Banafshe Felfeliyan, Yuyue Zhou +5
Foundation models like the segment anything model require high-quality manual prompts for medical image segmentation, which is time-consuming and requires expertise. SAM and its va…
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure
Assefa Seyoum Wahd
In this paper, we present a novel approach that combines deep metric learning and synthetic data generation using diffusion models for out-of-distribution (OOD) detection. One popu…