Selected work

Representative Papers

A Persistent Homology Design Space for 3D Point Cloud Deep Learning

Apr 05, 2026

This work addresses the insufficient modeling of topological structures in existing deep learning approaches for 3D point clouds, where persistent homology has largely been relegated to peripheral roles. The authors introduce 3DPHDL—the first systematic design space that deeply integrates persistent homology as a structural inductive bias throughout the entire point cloud learning pipeline. This integration encompasses six well-defined injection points spanning simplicial complex construction, filtration strategies, persistence representations, and their coordination with backbone architectures. Through controlled experiments on PointNet, DGCNN, and Point Transformer—augmented with persistence diagrams, images, and landscapes—on ModelNet40 and ShapeNetPart, the approach significantly improves accuracy in classification and segmentation, enhances part consistency, and boosts robustness to noise and sampling variations, while also revealing inherent trade-offs between representational capacity and computational complexity.

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Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context

Jul 17, 2025

Existing emotion recognition methods suffer from key limitations: overt behavioral cues (e.g., facial expressions, speech) are easily feigned; physiological signals require invasive instrumentation; and gaze analysis often neglects environmental context. To address these, we propose a non-intrusive, continuous emotion recognition paradigm leveraging only a standard high-definition camera to simultaneously capture naturalistic gaze trajectories and head motion. For the first time, our approach deeply integrates gaze dynamics, environmental semantics, and temporal evolution into a unified spatial–semantic–temporal behavioral model. It operates implicitly—requiring no user cooperation or specialized sensors—to decode affective states. Experimental results demonstrate high robustness, real-time performance, low cost, and strong scalability in unconstrained real-world settings. This work advances computational affective science by formalizing emotion as an emergent product of human–environment interaction, offering a novel, scalable, and ecologically valid framework for implicit affective computing.

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Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts

Jul 02, 2025

This paper addresses the challenge of reconciling data-driven efficiency of machine learning with humanistic subjectivity—such as emotion, culture, and aesthetics—in spatial design. We propose a human–AI co-design framework that deeply integrates multimodal machine learning (including generative and optimization algorithms) with user-centered design principles: algorithms handle pattern recognition and iterative solution generation, while human designers retain agency over value judgment, empathic response, and cultural interpretation. Our key innovation is an interpretable, human-intervenable design闭环 that enables concurrent functional optimization and affective iteration. Empirical validation in office and residential contexts demonstrates significant improvements in functional rationality, cultural appropriateness, and user emotional resonance. The framework provides a reproducible methodological pathway for humanistic, AI-augmented design.

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Pig behavior dataset and Spatial-temporal perception and enhancement networks based on the attention mechanism for pig behavior recognition

Mar 12, 2025

To address the longstanding absence of publicly available, fine-grained video datasets for porcine behavior recognition, this study introduces the first open-source video dataset comprising 13 welfare-critical pig behaviors. We further propose ST-ANet, an attention-guided spatiotemporal awareness and enhancement network featuring a two-stage architecture: Stage I localizes behavior-relevant regions and models individual and interactive dynamics via spatiotemporal graph convolution; Stage II incorporates feature recalibration and long-range temporal modeling to strengthen spatiotemporal dependencies. Evaluated on our curated dataset, ST-ANet achieves a mean Average Precision (mAP) of 75.92%, outperforming the best conventional method by 8.17 percentage points. The framework significantly improves accuracy in individual behavior recognition and demonstrates enhanced generalizability across diverse farming scenarios.

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Recent publications

Latest Papers

A Persistent Homology Design Space for 3D Point Cloud Deep Learning

Apr 05, 2026

This work addresses the insufficient modeling of topological structures in existing deep learning approaches for 3D point clouds, where persistent homology has largely been relegated to peripheral roles. The authors introduce 3DPHDL—the first systematic design space that deeply integrates persistent homology as a structural inductive bias throughout the entire point cloud learning pipeline. This integration encompasses six well-defined injection points spanning simplicial complex construction, filtration strategies, persistence representations, and their coordination with backbone architectures. Through controlled experiments on PointNet, DGCNN, and Point Transformer—augmented with persistence diagrams, images, and landscapes—on ModelNet40 and ShapeNetPart, the approach significantly improves accuracy in classification and segmentation, enhances part consistency, and boosts robustness to noise and sampling variations, while also revealing inherent trade-offs between representational capacity and computational complexity.

0 citationsRead paper

Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context

Jul 17, 2025

Existing emotion recognition methods suffer from key limitations: overt behavioral cues (e.g., facial expressions, speech) are easily feigned; physiological signals require invasive instrumentation; and gaze analysis often neglects environmental context. To address these, we propose a non-intrusive, continuous emotion recognition paradigm leveraging only a standard high-definition camera to simultaneously capture naturalistic gaze trajectories and head motion. For the first time, our approach deeply integrates gaze dynamics, environmental semantics, and temporal evolution into a unified spatial–semantic–temporal behavioral model. It operates implicitly—requiring no user cooperation or specialized sensors—to decode affective states. Experimental results demonstrate high robustness, real-time performance, low cost, and strong scalability in unconstrained real-world settings. This work advances computational affective science by formalizing emotion as an emergent product of human–environment interaction, offering a novel, scalable, and ecologically valid framework for implicit affective computing.

0 citationsRead paper

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts

Jul 02, 2025

This paper addresses the challenge of reconciling data-driven efficiency of machine learning with humanistic subjectivity—such as emotion, culture, and aesthetics—in spatial design. We propose a human–AI co-design framework that deeply integrates multimodal machine learning (including generative and optimization algorithms) with user-centered design principles: algorithms handle pattern recognition and iterative solution generation, while human designers retain agency over value judgment, empathic response, and cultural interpretation. Our key innovation is an interpretable, human-intervenable design闭环 that enables concurrent functional optimization and affective iteration. Empirical validation in office and residential contexts demonstrates significant improvements in functional rationality, cultural appropriateness, and user emotional resonance. The framework provides a reproducible methodological pathway for humanistic, AI-augmented design.

0 citationsRead paper

Pig behavior dataset and Spatial-temporal perception and enhancement networks based on the attention mechanism for pig behavior recognition

Mar 12, 2025

To address the longstanding absence of publicly available, fine-grained video datasets for porcine behavior recognition, this study introduces the first open-source video dataset comprising 13 welfare-critical pig behaviors. We further propose ST-ANet, an attention-guided spatiotemporal awareness and enhancement network featuring a two-stage architecture: Stage I localizes behavior-relevant regions and models individual and interactive dynamics via spatiotemporal graph convolution; Stage II incorporates feature recalibration and long-range temporal modeling to strengthen spatiotemporal dependencies. Evaluated on our curated dataset, ST-ANet achieves a mean Average Precision (mAP) of 75.92%, outperforming the best conventional method by 8.17 percentage points. The framework significantly improves accuracy in individual behavior recognition and demonstrates enhanced generalizability across diverse farming scenarios.

0 citationsRead paper