Multi Label Image Classification

Multi-label image classification aims to identify multiple objects or attributes within a single image, a task complicated by label dependencies and imbalanced datasets. Current research focuses on improving model performance through techniques like hierarchical architectures (e.g., transformers), vision-language model integration (e.g., CLIP), and advanced loss functions (e.g., asymmetric loss) to address class imbalance and noisy labels. These advancements are crucial for applications ranging from medical image diagnosis to object recognition in complex scenes, improving accuracy and efficiency in diverse fields.