Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection

Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection

GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection Research Background and Problem Statement In real-world applications, machine learning models often face changes in data distribution, such as the emergence of new categories. This phenomenon is known as “Out-of-Distribution Detection (OOD).” To ensure the...

Combating Label Noise with a General Surrogate Model for Sample Selection

Academic Background and Problem Statement With the rapid development of Deep Neural Networks (DNNs), visual intelligence systems have made significant progress in tasks such as image classification, object detection, and video understanding. However, these breakthroughs rely heavily on the collection of high-quality annotated data, which is often t...