# More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning

## TL;DR

This paper introduces a new framework for single positive multi-label learning that improves label reliability and model performance by using a robust loss function and dynamic pseudo-labeling strategies, achieving state-of-the-art results.

## Contribution

It proposes the GPR Loss and DAMP techniques within the AEVLP framework to enhance learning from partially annotated multi-label data.

## Key findings

- Significant performance improvements on four benchmark datasets.
- State-of-the-art results in multi-label classification.
- Effective handling of noisy pseudo-labels.

## Abstract

Multi-label learning is a challenging computer vision task that requires assigning multiple categories to each image. However, fully annotating large-scale datasets is often impractical due to high costs and effort, motivating the study of learning from partially annotated data. In the extreme case of Single Positive Multi-Label Learning (SPML), each image is provided with only one positive label, while all other labels remain unannotated. Traditional SPML methods that treat missing labels as unknown or negative tend to yield inaccuracies and false negatives, and integrating various pseudo-labeling strategies can introduce additional noise. To address these challenges, we propose the Generalized Pseudo-Label Robust Loss (GPR Loss), a novel loss function that effectively learns from diverse pseudo-labels while mitigating noise. Complementing this, we introduce a simple yet effective Dynamic Augmented Multi-focus Pseudo-labeling (DAMP) technique. Together, these contributions form the Adaptive and Efficient Vision-Language Pseudo-Labeling (AEVLP) framework. Extensive experiments on four benchmark datasets demonstrate that our framework significantly advances multi-label classification, achieving state-of-the-art results.