Multi-Label Zero-Shot Learning Via Contrastive Label-Based Attention.

Multi-label zero-shot learning (ML-ZSL) strives to recognize all objects in an image, regardless of whether they are present in the training data. Recent methods incorporate an attention mechanism to locate labels in the image and generate class-specific semantic information. However, the attention mechanism built on visual features treats label embeddings equally in the prediction score, leading to severe semantic ambiguity. This study focuses on efficiently utilizing semantic information in the attention mechanism. We propose a contrastive label-based attention method (CLA) to associate each label with the most relevant image regions. Specifically, our label-based attention, guided by the latent label embedding, captures discriminative image details. To distinguish region-wise correlations, we implement a region-level contrastive loss. In addition, we utilize a global feature alignment module to identify labels with general information. Extensive experiments on two benchmarks, NUS-WIDE and Open Images, demonstrate that our CLA outperforms the state-of-the-art methods. Especially under the ZSL setting, our method achieves 2.0% improvements in mean Average Precision (mAP) for NUS-WIDE and 4.0% for Open Images compared with recent methods.