Activity Label

Activity labeling in various domains, from image classification to human activity recognition, focuses on accurately assigning labels to data points, often with the goal of improving model performance and interpretability. Current research emphasizes addressing challenges like data scarcity, label noise, and computational cost through techniques such as multiple instance learning (MIL), attention mechanisms, and the incorporation of temporal and semantic relationships between labels. These advancements are crucial for improving the accuracy and robustness of machine learning models across diverse applications, including healthcare, autonomous systems, and environmental monitoring.