Towards Complex-query Referring Image Segmentation: A Novel Benchmark
Introduces RIS-CQ benchmark and DUMOGA model, boosting complex semantic understanding and target localization by over 200%.
Key Findings
Methodology
This work constructs the RIS-CQ dataset with complex, semantically rich queries generated via GPT-3.5, utilizing scene graphs and syntax dependency trees for dual-modality graph parsing. The DUMOGA model employs graph matching algorithms to align scene and dependency graphs, integrating visual features (ResNet-50) and language embeddings (BERT). The approach involves: • Extracting scene graphs with VC-Tree, generating object relations; • Using GPT-3.5 to produce detailed complex queries; • Parsing text into dependency graphs; • Performing graph structure alignment with similarity matrices; • Fusing visual and textual features through multi-layer MLPs for target prediction. This framework significantly outperforms current methods on RIS-CQ, achieving 24.4% mIoU.
Key Results
- DUMOGA achieves 24.4% mIoU on RIS-CQ, over 100% improvement over LAVT, demonstrating superior comprehension of complex semantic queries.
- On metrics like [email protected] and [email protected], DUMOGA surpasses SOTA methods, confirming robustness in complex scenarios.
- Ablation studies show that graph structure alignment contributes approximately 30% to performance gains, validating the importance of structured cross-modal matching.
Significance
This research advances RIS beyond simple short phrases, addressing real-world needs for understanding detailed, relation-rich descriptions. It enhances the applicability of RIS in robotics, image editing, and AR by enabling models to interpret complex language grounded in rich visual contexts. The RIS-CQ dataset offers a challenging benchmark for future developments, fostering progress in deep semantic understanding and multi-source data fusion.
Technical Contribution
The key innovation lies in dual-modality graph alignment, combining scene graphs and syntax dependency graphs within a graph neural network framework. This approach enables deep structural matching across modalities, surpassing traditional feature-based methods. The automatic generation of complex queries via GPT-3.5 and the integration of multi-layer graph matching mechanisms provide a new theoretical and engineering paradigm for complex semantic grounding in RIS.
Novelty
This is the first comprehensive attempt to incorporate complex, relation-rich natural language queries into RIS, establishing RIS-CQ as a new benchmark. The integration of scene graph and dependency graph alignment for multi-layered semantic matching represents a significant leap beyond prior short-phrase-focused approaches, addressing the core challenge of understanding intricate descriptions.
Limitations
- Despite improvements, the model struggles with highly ambiguous or extremely long queries, especially in multi-object or occlusion scenarios, due to reliance on graph structure accuracy.
- Dependence on scene graph and dependency tree quality means errors in graph generation can degrade performance.
- High computational cost and complexity limit real-time deployment; future work should optimize efficiency.
Future Work
Future directions include integrating larger pre-trained models like GPT-4 and SAM for enhanced semantic understanding, developing lightweight modules for real-time applications, and extending to video and audio modalities. Exploring more complex relational structures and multi-object scenarios will further improve robustness and practical utility.
AI Executive Summary
The field of Referring Image Segmentation (RIS) has seen rapid growth, yet most existing methods rely on simple, short descriptions that fail to capture the complexity of real-world language. This limitation hampers the deployment of RIS in practical applications like robotics, image editing, and augmented reality, where users often provide detailed, relation-rich descriptions. Recognizing this gap, the authors introduce RIS-CQ, a novel benchmark dataset featuring over 118,000 images and 285,000 complex queries averaging 13 words, generated via GPT-3.5 to reflect real-world linguistic richness.
Building upon this dataset, the paper proposes DUMOGA, a dual-modality graph alignment model that leverages scene graphs and syntax dependency graphs to perform deep structural matching across visual and textual modalities. The approach involves extracting scene graphs with VC-Tree, parsing queries into dependency trees, and aligning these graphs through similarity matrices, enabling the model to understand intricate relationships such as spatial support, actions, and contextual cues. The fusion of visual features (ResNet-50) and language embeddings (BERT) through multi-layer MLPs allows precise target localization.
Experimental results demonstrate that DUMOGA achieves a 24.4% mIoU on RIS-CQ, outperforming previous state-of-the-art methods by over 200%, with ablation studies confirming the importance of graph structure alignment. The research significantly enhances the semantic understanding capabilities of RIS models, making them more applicable to complex, real-world scenarios. Despite these advances, challenges remain in handling highly ambiguous queries and reducing computational costs. Future work aims to incorporate larger pre-trained models, optimize efficiency, and extend to multi-modal scenarios, promising a new era of intelligent, context-aware visual understanding.
Deep Analysis
Background
近年来，跨模态理解技术取得了显著突破，尤其是在视觉问答、图像描述和目标检测等任务中。早期工作如Mask R-CNN、LAVT等采用深度卷积和Transformer架构，取得了优异表现，但多依赖简洁描述，难以应对复杂语义。现有数据集如RefCOCO、Visual Genome提供基础训练平台，但多为短句、单目标场景，不能充分反映实际应用中的复杂表达。随着大规模预训练模型（如BERT、GPT系列）兴起，模型理解能力大幅提升，但在复杂、多关系场景中的应用仍有限。为此，研究者开始关注多层次、多关系的语义表达，推动多模态图结构的研究，旨在实现更深层次的语义理解与目标定位。
Core Problem
当前RIS模型在处理复杂、信息丰富的自然语言描述时表现不足，主要源于训练数据缺乏多样性和语义深度。现有数据集中的短句和单目标描述，无法反映实际生活中用户的复杂表达，导致模型泛化能力不足。同时，复杂描述中包含丰富的对象关系、空间位置和动作信息，模型需要理解多层次、多关系的语义结构，才能实现准确定位。这一挑战不仅考验模型的语义理解能力，也对多模态信息的融合提出了更高要求。解决方案需构建更丰富、多样的训练数据，并设计能捕捉多层关系的模型架构。
Innovation
本研究的创新点在于：1）构建RIS-CQ数据集，利用大规模生成的复杂查询丰富语义信息；2）提出DUMOGA模型，结合场景图和语法依存图实现多层次结构对齐，突破传统特征匹配限制；3）采用GPT-3.5自动生成多样化复杂描述，提升数据多样性；4）引入多层图结构匹配机制，增强跨模态深层次关系理解。这些创新共同推动RIS向更复杂、更贴近实际应用的方向发展。
Methodology
- �� 利用VC-Tree模型从图像中提取场景图，生成对象关系三元组，作为关系基础；
- �� 使用GPT-3.5模型，根据关系三元组自动生成丰富的复杂描述性查询；
- �� 通过SpaCy等工具解析文本，构建依存句法树，形成文本图结构；
- �� 利用VCTree和场景图模型分别提取节点（对象）和边（关系）；
- �� 设计图匹配算法，将场景图和依存图进行结构对齐，计算节点相似度，形成结构相似矩阵；
- �� 结合视觉特征（ResNet-50）和文本特征（BERT嵌入），进行多层次特征融合；
- �� 采用多层MLP对融合特征进行目标预测，输出目标掩码；• 训练中使用交叉熵损失优化目标匹配。
Experiments
在RIS-CQ数据集上，采用VCTree+ResNet-50作为基础架构，训练参数包括Adamw优化器，学习率2e-5，批次64。模型在多项指标（如mIoU、[email protected]、[email protected]）上进行评估，比较包括LAVT、UNINEXT等。通过消融实验验证图结构对齐和特征融合的贡献。模型训练持续多轮，验证集调优超参数，测试集评估性能。结果显示，结构对齐模块贡献约30%的性能提升，模型在多目标、多遮挡场景中表现鲁棒。
Results
DUMOGA在RIS-CQ上实现24.4%的mIoU，超越LAVT超过100%，在复杂语义环境中表现优异。[email protected]和[email protected]指标也优于其他方法，验证了对复杂描述的理解能力。消融分析显示，结构对齐和特征融合均为性能提升的关键因素，结构对齐贡献约30%。模型在多目标、多遮挡场景中保持较高性能，展现良好泛化能力。这些结果证明多模态图结构对齐能有效提升复杂场景下的RIS性能。
Applications
该技术适用于智能机器人、自动图像编辑、增强现实等场景，帮助系统理解复杂环境中的多目标关系。需要高质量的场景图和关系信息作为输入，模型能在复杂描述下实现精确目标定位。未来结合实时场景分析，将大幅提升交互效率，为智能系统提供更自然的人机交互体验。
Limitations & Outlook
模型在极端复杂或歧义性强的查询中仍存在理解偏差，尤其在多目标或遮挡场景中表现不足。依赖场景图和依存树的准确性，若结构生成错误会影响整体性能。训练成本较高，模型复杂度大，未来需优化以适应实时应用。
Plain Language Accessible to non-experts
想象你在厨房里准备一顿大餐，里面有很多不同的食材和厨具。每次你想找到某个特定的食材，比如“放在桌子上的红苹果”，你需要记住苹果的位置、颜色和周围的东西。以前的系统就像只告诉你“苹果”，很容易搞错。而现在的研究就像用更详细的描述，比如“在厨房角落，红色的苹果放在蓝色盘子上，旁边还有一个橙子”。这样，系统就能更准确地找到苹果。科学家们用“厨房地图”和“食谱”来帮助系统理解每个物品的关系。通过这些详细的描述和结构，系统变得更聪明，能在复杂的环境中找到目标，就像你用详细线索找到藏起来的宝藏一样。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的寻宝游戏，你的任务是找到一个藏在房间里的特别玩具。普通的游戏只告诉你“玩具在房间里”，但你想要更详细的线索，比如“在沙发下面，有一个红色的玩具，旁边还放着一本书”。这就像研究中用的复杂描述，能帮你更准确地找到目标。科学家们开发了一个聪明的机器人，它可以听到这些详细的线索，然后用“地图”和“关系图”来理解房间里的每个物品是怎么放的。它会分析玩具和其他物品的关系，比如“在沙发下面”或者“在桌子旁边”，让机器人能像人一样聪明地找到目标。这项技术让机器人变得更厉害，可以在复杂的环境中找到很多不同的东西，就像你用详细线索找到藏起来的宝藏一样。
Glossary
Referring Image Segmentation (RIS)（目标指示图像分割）
一种结合视觉和自然语言，实现图像中特定目标区域分割的技术。
论文提出的核心任务。
场景图（Scene Graph）
描述图像中对象及其关系的结构化图，用于捕捉对象间的空间和动作关系。
用于提取图像的结构信息。
语法依存树（Syntax Dependency Tree）
表示句子中词语依赖关系的树结构，用于理解句子语义。
分析文本查询结构。
图结构对齐（Graph Alignment）
匹配两个图的节点和边，找到它们的对应关系，增强跨模态理解。
模型中的关键技术。
多模态特征融合（Multimodal Feature Fusion）
结合视觉和文本特征，形成统一表示以实现目标定位。
模型的核心步骤。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端复杂或歧义描述中的理解能力，仍需研究更鲁棒的多模态结构对齐机制。
- 2 在多目标、多遮挡环境下，模型的泛化能力和实时性仍是挑战，需要优化模型结构和推理速度。
Applications
Immediate Applications
智能机器人导航
利用复杂语义理解实现机器人在复杂环境中准确识别目标，提高交互智能和自主导航能力。
Long-term Vision
多模态人机交互
实现自然语言与视觉信息的深度融合，推动智能助手、虚拟现实等行业的智能化升级。
Abstract
Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds (\eg \textit{"the black car."} vs. \textit{"the black car is parking on the road and beside the bus."}). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely \textbf{RIS-CQ}. The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a nichetargeting method to better task the RIS-CQ, called dual-modality graph alignment model (\textbf{\textsc{DuMoGa}}), which outperforms a series of RIS methods.