发表机构
Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对开放词汇目标检测中固定连接器在层次语义表示上的局限，提出InterHier，利用前置可学习上下文全局引导层次提示词，优化视觉与文本嵌入对齐，持续提升性能并可无缝集成。
AI 中文摘要
在本文中，我们研究了开放词汇目标检测中层次语义表示里固定手工连接器的局限性。现有方法通过在相邻的父/子类别之间放置固定连接器，来建立基础类别与未见新颖类别之间的语义关系。然而，这种固定连接器可能无法最优地捕获语义层次内的关系。为解决这一局限，我们提出了互连层次语义表示（InterHier），该方法利用一个前置的可学习上下文来全局指导包含层次关系的提示词的解释。InterHier 分两个主要阶段运行。首先，它通过整合父/子类别并前置一个可学习上下文来构建层次感知的提示词。其次，它优化这个可学习上下文，以使视觉区域嵌入和文本嵌入对齐。InterHier 在依赖固定连接器的方法上持续提升了性能，并且可以无缝集成到现有的开放词汇目标检测模型中。在开放词汇目标检测基准上的实验表明，InterHier 达到了与最先进方法相竞争的性能。
英文摘要
In this paper, we investigate the limitations of fixed, hand-crafted connectors in hierarchical semantic representations for open-vocabulary object detection. Existing methods establish semantic relationships between base categories and unseen novel categories by placing a fixed connector between adjacent super-/sub-categories. However, such fixed connectors may not optimally capture the relationships within a semantic hierarchy. To address this limitation, we propose interconnected hierarchical semantic representations (InterHier), which utilize a prepended learnable context to globally guide the interpretation of prompts containing hierarchical relationships. InterHier operates in two main stages. First, it constructs a hierarchy-aware prompt by integrating super-/sub-categories and prepending a learnable context. Second, it optimizes this learnable context to align visual region embeddings and textual embeddings. InterHier consistently improves performance over methods that rely on fixed connectors and can be seamlessly integrated into existing open-vocabulary object detection models. Experiments on open-vocabulary object detection benchmarks demonstrate that InterHier achieves competitive performance against state-of-the-art methods.
Comments12 pages, 6 figures. Published in IEEE Access
Journal refIEEE Access, vol. 14, pp. 14709-14721, 2026
DOI：10.1109/ACCESS.2026.3655392
