HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment

• URL: http://arxiv.org/abs/2511.06653v1
• Date: Mon, 10 Nov 2025 03:04:36 GMT
• Title: HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment
• Authors: Ruijia Wu, Ping Chen, Fei Shen, Shaoan Zhao, Qiang Hui, Huanlin Gao, Ting Lu, Zhaoxiang Liu, Fang Zhao, Kai Wang, Shiguo Lian,
• Abstract summary: HiMo-CLIP is a representation-level framework that enhances CLIP-style models without modifying the encoder architecture.<n>HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, and a monotonicity-aware contrastive loss (MoLo)<n>Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines.
• Score: 13.584710249222105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. However, these models often treat text as flat sequences, limiting their ability to handle complex, compositional, and long-form descriptions. In particular, they fail to capture two essential properties of language: semantic hierarchy, which reflects the multi-level compositional structure of text, and semantic monotonicity, where richer descriptions should result in stronger alignment with visual content.To address these limitations, we propose HiMo-CLIP, a representation-level framework that enhances CLIP-style models without modifying the encoder architecture. HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, enabling flexible, batch-aware alignment across different semantic granularities, and a monotonicity-aware contrastive loss (MoLo) that jointly aligns global and component-level representations, encouraging the model to internalize semantic ordering and alignment strength as a function of textual completeness.These components work in concert to produce structured, cognitively-aligned cross-modal representations. Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines, particularly under long or compositional descriptions. The code is available at https://github.com/UnicomAI/HiMo-CLIP.

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