LIHE：用于广义弱监督指代表达理解的语言实例拆分双曲-欧几里得框架
LIHE: Linguistic Instance-Split Hyperbolic-Euclidean Framework for Generalized Weakly-Supervised Referring Expression Comprehension
AI总结：
针对现有弱监督指代表达理解受一对一映射假设限制的问题，提出WGREC任务及LIHE框架，通过指代解耦和HEMix混合相似度模块，在gRefCOCO等数据集建立首个WGREC基线，提升标准REC基准IoU@0.5达2.5%。
摘要：
现有弱监督指代表达理解（WREC）方法虽有效，但受限于一对一映射假设，难以处理现实场景中对应零或多个目标的表达式。为此，我们提出弱监督广义指代表达理解任务（WGREC），这是一种更实用的范式，可处理具有可变数量指代对象的表达式。然而，将WREC扩展到WGREC面临两大挑战：监督信号模糊（弱图像级监督不足以训练模型推断指代对象的正确数量和身份）和语义表示坍缩（标准欧几里得相似度迫使层次相关概念形成非判别性簇，模糊类别边界）。为应对这些挑战，我们提出名为语言实例拆分双曲-欧几里得（LIHE）的新型WGREC框架，分为两个阶段：第一阶段（指代解耦）预测目标对象数量并将复杂表达式分解为简单子表达式；第二阶段（指代定位）使用HEMix（我们创新的混合相似度模块，协同结合欧几里得邻近的精确对齐能力与双曲几何的层次建模优势）定位这些子表达式。这种混合方法有效防止语义坍缩，同时保留相关概念间的细粒度区分。大量实验表明，LIHE在gRefCOCO和Ref-ZOM上建立首个有效的弱监督WGREC基线，而HEMix在标准REC基准上实现持续改进，IoU@0.5最多提升2.5%。代码可在https://anonymous.4open.science/r/LIHE获取。
英文摘要：
Existing Weakly-Supervised Referring Expression Comprehension (WREC) methods, while effective, are fundamentally limited by a one-to-one mapping assumption, hindering their ability to handle expressions corresponding to zero or multiple targets in realistic scenarios. To bridge this gap, we introduce the Weakly-Supervised Generalized Referring Expression Comprehension task (WGREC), a more practical paradigm that handles expressions with variable numbers of referents. However, extending WREC to WGREC presents two fundamental challenges: supervisory signal ambiguity, where weak image-level supervision is insufficient for training a model to infer the correct number and identity of referents, and semantic representation collapse, where standard Euclidean similarity forces hierarchically-related concepts into non-discriminative clusters, blurring categorical boundaries. To tackle these challenges, we propose a novel WGREC framework named Linguistic Instance-Split Hyperbolic-Euclidean (LIHE), which operates in two stages. The first stage, Referential Decoupling, predicts the number of target objects and decomposes the complex expression into simpler sub-expressions. The second stage, Referent Grounding, then localizes these sub-expressions using HEMix, our innovative hybrid similarity module that synergistically combines the precise alignment capabilities of Euclidean proximity with the hierarchical modeling strengths of hyperbolic geometry. This hybrid approach effectively prevents semantic collapse while preserving fine-grained distinctions between related concepts. Extensive experiments demonstrate LIHE establishes the first effective weakly supervised WGREC baseline on gRefCOCO and Ref-ZOM, while HEMix achieves consistent improvements on standard REC benchmarks, improving IoU@0.5 by up to 2.5\%. The code is available at https://anonymous.4open.science/r/LIHE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
