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Shandong University of Science and Technology

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Research library26linked papers
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Selected work

Representative Papers

TeaMatch: Teachable Cross-Modal Representation Learning for 2D-3D Matching

Aug 10, 2026

This work addresses the challenge of establishing robust 2D–3D correspondences under severe degradation conditions such as noise, low overlap, and structural ambiguity. The authors propose TeaMatch, a novel framework that introduces teachability into cross-modal representation learning for the first time. By employing a task-oriented weak student to simulate typical failure modes and optimizing representations to recover the teacher’s features, TeaMatch enhances structural consistency and robustness. The approach integrates a teacher–student architecture with correspondence-level constraints and geometry-aware regularization, seamlessly fitting into coarse-to-fine matching pipelines without incurring additional inference overhead. Extensive experiments demonstrate that TeaMatch achieves state-of-the-art performance across multiple challenging 2D–3D matching benchmarks, significantly improving matching robustness.

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Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding

Aug 06, 2026

This work addresses the challenge of evaluating cross-conceptual understanding in multimodal large language models (MLLMs) within open-ended creative tasks. To this end, the authors propose the C4 framework, which formalizes cross-conceptual creativity as a measurable cognitive task by leveraging Chinese idioms as conceptual carriers. They construct C4-Eval, a benchmark comprising both synthetic and human-authored samples, accompanied by structured difficulty metrics, explicit reference answers, and human-validated bridging paths. Through techniques including cross-conceptual network modeling, candidate constraint enforcement, and prompt-based interventions, the study systematically assesses MLLMs’ creative decoding capabilities. Experimental results reveal that even the strongest closed-source models achieve only around 50% accuracy on the primary task, while open-source counterparts lag significantly; candidate constraints yield notable performance gains, whereas bridging prompts offer limited improvement, underscoring fundamental limitations in current models’ capacity for creative conceptual synthesis.

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SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2)

Aug 03, 2026

This study addresses the limited performance of existing automatic sleep staging methods in ambiguous and transitional stages—particularly N1—due to insufficient modeling of fine-grained intra-epoch structures and cross-regional spectral dependencies. To overcome this, the authors propose a dual-stream hierarchical context-aware framework that jointly processes raw time-domain EEG signals and multi-scale time-frequency representations. Convolutional encoders capture waveform details, while Swin Transformers model local spectro-temporal features and long-range dependencies. A bidirectional context module then fuses multi-branch features to explicitly refine both intra-epoch representations and inter-epoch temporal relationships. Evaluated on the Sleep-EDF-20/78 and SHHS datasets, the method achieves state-of-the-art performance, significantly improving robustness and accuracy in identifying N1 and other transitional sleep stages.

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Recent publications

Latest Papers

TeaMatch: Teachable Cross-Modal Representation Learning for 2D-3D Matching

Aug 10, 2026

This work addresses the challenge of establishing robust 2D–3D correspondences under severe degradation conditions such as noise, low overlap, and structural ambiguity. The authors propose TeaMatch, a novel framework that introduces teachability into cross-modal representation learning for the first time. By employing a task-oriented weak student to simulate typical failure modes and optimizing representations to recover the teacher’s features, TeaMatch enhances structural consistency and robustness. The approach integrates a teacher–student architecture with correspondence-level constraints and geometry-aware regularization, seamlessly fitting into coarse-to-fine matching pipelines without incurring additional inference overhead. Extensive experiments demonstrate that TeaMatch achieves state-of-the-art performance across multiple challenging 2D–3D matching benchmarks, significantly improving matching robustness.

0 citationsRead paper

Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding

Aug 06, 2026

This work addresses the challenge of evaluating cross-conceptual understanding in multimodal large language models (MLLMs) within open-ended creative tasks. To this end, the authors propose the C4 framework, which formalizes cross-conceptual creativity as a measurable cognitive task by leveraging Chinese idioms as conceptual carriers. They construct C4-Eval, a benchmark comprising both synthetic and human-authored samples, accompanied by structured difficulty metrics, explicit reference answers, and human-validated bridging paths. Through techniques including cross-conceptual network modeling, candidate constraint enforcement, and prompt-based interventions, the study systematically assesses MLLMs’ creative decoding capabilities. Experimental results reveal that even the strongest closed-source models achieve only around 50% accuracy on the primary task, while open-source counterparts lag significantly; candidate constraints yield notable performance gains, whereas bridging prompts offer limited improvement, underscoring fundamental limitations in current models’ capacity for creative conceptual synthesis.

0 citationsRead paper

SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2)

Aug 03, 2026

This study addresses the limited performance of existing automatic sleep staging methods in ambiguous and transitional stages—particularly N1—due to insufficient modeling of fine-grained intra-epoch structures and cross-regional spectral dependencies. To overcome this, the authors propose a dual-stream hierarchical context-aware framework that jointly processes raw time-domain EEG signals and multi-scale time-frequency representations. Convolutional encoders capture waveform details, while Swin Transformers model local spectro-temporal features and long-range dependencies. A bidirectional context module then fuses multi-branch features to explicitly refine both intra-epoch representations and inter-epoch temporal relationships. Evaluated on the Sleep-EDF-20/78 and SHHS datasets, the method achieves state-of-the-art performance, significantly improving robustness and accuracy in identifying N1 and other transitional sleep stages.

0 citationsRead paper