Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

We study a limited label problem and present a novel approach to Single-Positive Multi-label Learning. In the multi-label learning setting, a model learns to predict multiple labels or categories for a single input image. This contrasts with standard multi-class image classification, where the task is to predict a single label from many possible labels for an image. Single-Positive Multi-label Learning specifically considers learning to predict multiple labels when there is only one annotation per image in the training data. Multi-label learning is a more natural task than single-label learning because real-world data often involves instances belonging to multiple categories simultaneously; however, most computer vision datasets contain single labels due to the inherent complexity and cost of collecting multiple high-quality annotations per image. We propose a novel approach called Vision-Language Pseudo-Labeling, which uses a vision-language model, CLIP, to suggest strong positive and negative pseudo-labels. The experiment performance shows the effectiveness of the proposed model. Our code and data will be made publicly available at https://github.com/mvrl/VLPL.