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Yu Fei

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AAAI Conference 2026 Conference Paper

Open-World 3D Scene Graph Generation for Retrieval-Augmented Reasoning

  • Yu Fei
  • Quan Deng
  • Shengeng Tang
  • Li Yuehua
  • Lechao Cheng

Open-world 3D scene understanding is fundamentally challenging for vision and robotics, due to the constraints of closed-vocabulary supervision and static annotations. To address this, we propose a unified framework for Open-World 3D Scene Graph Generation with Retrieval-Augmented Reasoning, which enables generalizable and interactive 3D scene understanding. Our method integrates vision-language models with retrieval-based reasoning to support multimodal exploration and language-guided interaction. The framework comprises two key components: (1) a dynamic scene graph generation module that detects objects and infers semantic relationships without fixed label sets, and (2) a retrieval-augmented reasoning pipeline that encodes scene graphs into a vector database to support text/image-conditioned queries. We evaluate our method on 3DSSG and Replica benchmarks across four tasks—scene question answering, visual grounding, instance retrieval, and task planning—demonstrating robust generalization and superior performance in diverse environments. Our results highlight the effectiveness of combining open-vocabulary perception with retrieval-based reasoning for scalable 3D scene understanding.

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JBHI Journal 2025 Journal Article

Interval-Shared Information Integration and False-Negative Association Reduction in Multi-Source MiRNA-Disease Association Prediction

  • Qinghang Cui
  • Honglie Guo
  • Yueyi Cai
  • Yu Fei
  • Shunfang Wang

Numerous studies have demonstrated that microRNAs (miRNAs) play crucial roles in the development and progression of various diseases, making the identification of miRNA-disease association (MDA) essential for understanding human disease etiology. While several computational models have been developed to predict MDAs, challenges persist—particularly the limited consideration of information interactions among multi-source similarities and the presence of “false-negative” associations in the original topology. To address these issues, we propose ISFNMDA, a model designed to infer potential MDAs by leveraging multi-view collaborative learning for feature extraction and optimizing association topology through graph structure momentum contrastive learning. Specifically, multi-source similarities of miRNAs and diseases are mapped into a unified feature space via encoders. The Pearson correlation coefficient is employed to derive pairwise constraints between nodes, facilitating information interactions and constructing interval-shared information constraints. Subsequently, an inference graph learner models the representations to generate an inferred graph topology. By maximizing mutual information between the inferred topology and the original “false-negative” associations through momentum contrastive learning, the model effectively reduces spurious correlations. The final comprehensive representations and optimized graph structure are then used to predict potential MDAs. Experimental results demonstrate that ISFNMDA outperforms existing methods, and case studies further validate its predictive capability.

Details DOI
v2026.09.13