Hierarchical Vision–Language Pre-Training with Freezing Strategy for Multi-Level Semantic Alignment

Vision–language pre-training (VLP) faces challenges in aligning hierarchical textual semantics (words/phrases/sentences) with multi-scale visual features (objects/relations/global context). We propose a hierarchical VLP model (HieVLP) that addresses such challenges through semantic decomposition and progressive alignment. Textually, a semantic parser deconstructs captions into word-, phrase-, and sentence-level components, which are encoded via hierarchical BERT layers. Visually, a Swin Transformer extracts object- (local), relation- (mid-scale), and global-level features through shifted window hierarchies. During pre-training, a freezing strategy sequentially activates text layers (sentence→phrase→word), aligning each with the corresponding visual scales via contrastive and language modeling losses. The experimental evaluations demonstrate that HieVLP outperforms hierarchical baselines across various tasks, with the performance improvements ranging from approximately 3.2% to 11.2%. In the image captioning task, HieVLP exhibits an average CIDEr improvement of around 7.2% and a 2.1% improvement in the SPICE metric. For image–text retrieval, it achieves recall increases of 4.7–6.8%. In reasoning tasks, HieVLP boosts accuracy by 2.96–5.8%. These results validate that explicit multi-level alignment enables contextually coherent caption generation and precise cross-modal reasoning.