Object image retrieval based on self-supervised learning and zero-shot learning
A novel three-stage framework that integrates self-supervised contrastive pre-training with a dual-branch attention-driven architecture and employs an asymmetric deep hashing layer that promotes both high-level semantic consistency and computational efficiency through binary Hamming distance matching is proposed.