Multi Label Image Recognition

Multi-label image recognition aims to automatically assign multiple labels to a single image, reflecting its diverse content. Current research heavily utilizes vision-language models (VLMs) and focuses on improving cross-modal alignment between visual and textual information, often employing techniques like prompt tuning and graph convolutional networks to capture complex label relationships and handle incomplete or noisy annotations. These advancements are significant for applications requiring fine-grained image understanding, such as object detection in complex scenes and large-scale image indexing, and are driving progress in efficient training methods for data-scarce scenarios.