Dynamic Positive CLIP Distillation for Multi-Labeled Complementary Label Learning

Multi-labeled complementary label learning (MLCLL) trains multi-label classifiers from complementary labels, which indicate classes absent from a sample rather than its positive labels. Since verifying absent classes can be easier than exhaustively annotating all positive labels, MLCLL provides a cost-effective solution for multi-label learning (MLL). Existing methods mainly learn from complementary labels, which reveal only a subset of negative classes and provide no positive supervision. Our analysis shows that such non-exhaustive complementary supervision may leave residual uncertainty about the positive-label set, while only negatives labels may result in a degenerate solution that assigns low probabilities to all classes. Pretrained vision-language models offer a promising way to recover the missing positive semantics, but existing approaches typically require observed positive labels as semantic anchors and therefore cannot be directly applied to MLCLL. To address this limitation, we propose CoDi, a CLIP-based framework for MLCLL without positive labels. CoDi first employs a frozen CLIP teacher with positive-only prompts to construct positive evidence from cached global predictions and dynamically updated random-local and class-aware views guided by an exponential moving average (EMA) student. It then introduces reliability aware pseudo-positive learning to select, weight, and temporally stabilize pseudo-positive labels while preserving observed complementary labels as reliable negative supervision. The resulting positive knowledge is distilled into a standalone student model, enabling CLIP-free inference. Experiments on four benchmark datasets demonstrate the effectiveness of CoDi.