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Jiayuan Sun

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AAAI Conference 2026 Conference Paper

Multi-view Invariance Learning for 3D Scene Graph Pre-training via Collaborative Cross-Modal Regularization

Yucheng Huang
Luping Ji
Ruijie Xiao
Jiayuan Sun

3D scene graph generation is a pivotal task in scene understanding. Its performance is easy to be constrained by the limited availability of annotated data. Currently, the existing solutions on point cloud pre-training usually emphasize on object-centric representations while neglecting the predicate feature learning. This limitation significantly hinders their relational reasoning capabilities, as inter-object relationships are fundamentally governed by predicate features. To enhance 3D Scene Graphs Pre-training, this paper proposes a task-specific Multi-view Invariance Learning framework with Collaborative Cross-modal Regularization. In detail, the inherent horizontal-rotation invariance of 3D objects and their semantic relationships are leveraged to construct a self-supervised paradigm for triplet feature learning. Moreover, our framework harnesses the cross-modal prior knowledge from the vision-language model to regularize model optimization. It could further achieve the semantic discrimination via unsupervised deep clustering. To resolve the knowledge discrepancies arising from the pre-trained model in fine-tuning, a predicate adapter equipped with knowledge filtering gate is devised to selectively aggregate the predicate features of pre-trained model. Extensive experiments demonstrate that our framework is effective in boosting 3D scene graph generation performance, surpassing state-of-the-art ones.

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JBHI Journal 2023 Journal Article

Automatic Representative Frame Selection and Intrathoracic Lymph Node Diagnosis With Endobronchial Ultrasound Elastography Videos

Mingxing Xu
Junxiang Chen
Jin Li
Xinxin Zhi
Wenrui Dai
Jiayuan Sun
Hongkai Xiong

Endobronchial ultrasound (EBUS) elastography videos have shown great potential to supplement intrathoracic lymph node diagnosis. However, it is laborious and subjective for the specialists to select the representative frames from the tedious videos and make a diagnosis, and there lacks a framework for automatic representative frame selection and diagnosis. To this end, we propose a novel deep learning framework that achieves reliable diagnosis by explicitly selecting sparse representative frames and guaranteeing the invariance of diagnostic results to the permutations of video frames. Specifically, we develop a differentiable sparse graph attention mechanism that jointly considers frame-level features and the interactions across frames to select sparse representative frames and exclude disturbed frames. Furthermore, instead of adopting deep learning-based frame-level features, we introduce the normalized color histogram that considers the domain knowledge of EBUS elastography images and achieves superior performance. To our best knowledge, the proposed framework is the first to simultaneously achieve automatic representative frame selection and diagnosis with EBUS elastography videos. Experimental results demonstrate that it achieves an average accuracy of 81. 29% and area under the receiver operating characteristic curve (AUC) of 0. 8749 on the collected dataset of 727 EBUS elastography videos, which is comparable to the performance of the expert-based clinical methods based on manually-selected representative frames.

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