Optimizing multi-label image annotation: a hybrid CNN-Transformer deep learning approach

Multi-label image annotation remains a challenging task due to highly imbalanced label distributions, co-occurring semantic concepts, and the presence of rare or noisy annotations. To address these challenges, we present ReSwinNet, a hybrid CNN-Transformer framework that integrates ResNet-50 for fine-grained spatial feature extraction with the Swin Transformer for capturing long-range dependencies through hierarchical self-attention. A token projection mechanism maps multi-scale CNN features directly into Swin-compatible representations, bypassing standard patch embedding to preserve fine-grained spatial context that conventional patch partitioning discards. A frequency-based label pruning strategy further reduces annotation noise and class imbalance by retaining only semantically stable labels, significantly improving training convergence and predictive focus. Experiments are conducted on two benchmark datasets under multiple label settings to ensure transparent and fair evaluation. Under the full 374-label space on Corel-5K, ReSwinNet achieves a macro F1 of 0.491, surpassing 7 of 10 compared state-of-the-art methods and recording the highest recall among all compared models. Under the leakage-free 65-label pruned setting, ReSwinNet achieves a macro F1 of 0.701 and mAP of 0.742, outperforming all compared methods under identical experimental conditions. On IAPRTC-12, ReSwinNet achieves a macro F1 of 0.596 and mAP of 0.629 under the 198-label pruned setting, representing an improvement of over 14 percentage points in mAP relative to the strongest baseline. These results confirm the effectiveness of spatially-aware token fusion combined with frequency-based label filtering for large-scale multi-label image annotation.