Selected work

Representative Papers

AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles

Aug 07, 2026

This work addresses the challenge that existing physical adversarial camouflage methods struggle to simultaneously achieve visual naturalness and strong attack effectiveness against person detectors, while lacking flexible optimization of local patterns and their spatial arrangements. To overcome these limitations, we propose AdvTiles, a framework that jointly optimizes adversarial patterns and their layout through learnable tiles. Our approach introduces, for the first time, differentiable 3D Gaussian splatting rendering to enhance robustness across multiple viewpoints, lighting conditions, and backgrounds. A differentiable tile selection mechanism based on the straight-through Gumbel-Softmax estimator enables fine-grained texture control. Experiments demonstrate that AdvTiles achieves an average attack success rate of 86.2% across multiple person detectors, significantly outperforming state-of-the-art methods, and real-world wearable prototypes validate its effectiveness in practical scenarios.

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FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model

May 21, 2025

Physical field reconstruction (PFR) faces significant challenges in modeling long-range spatiotemporal dependencies under sparse sensor configurations, particularly for high-fidelity, full spatiotemporal reconstruction of time-varying flow fields (e.g., velocity, pressure, temperature). To address this, we propose FNO-Mamba—a novel hybrid architecture that synergistically integrates Fourier Neural Operators (FNOs) for efficient global spatial frequency-domain feature extraction with the Mamba state-space model for scalable, long-horizon temporal dependency modeling. This design overcomes classical trade-offs between computational complexity and reconstruction accuracy in temporal modeling, achieving linear-time inference complexity while preserving high-fidelity reconstruction. Evaluated on multi-scale flow field reconstruction tasks, FNO-Mamba consistently outperforms existing state-of-the-art methods, reducing average prediction error over long sequences by 21.6%. The framework establishes a new, efficient, and scalable paradigm for data-driven physical modeling.

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Recent publications

Latest Papers

AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles

Aug 07, 2026

This work addresses the challenge that existing physical adversarial camouflage methods struggle to simultaneously achieve visual naturalness and strong attack effectiveness against person detectors, while lacking flexible optimization of local patterns and their spatial arrangements. To overcome these limitations, we propose AdvTiles, a framework that jointly optimizes adversarial patterns and their layout through learnable tiles. Our approach introduces, for the first time, differentiable 3D Gaussian splatting rendering to enhance robustness across multiple viewpoints, lighting conditions, and backgrounds. A differentiable tile selection mechanism based on the straight-through Gumbel-Softmax estimator enables fine-grained texture control. Experiments demonstrate that AdvTiles achieves an average attack success rate of 86.2% across multiple person detectors, significantly outperforming state-of-the-art methods, and real-world wearable prototypes validate its effectiveness in practical scenarios.

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FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model

May 21, 2025

Physical field reconstruction (PFR) faces significant challenges in modeling long-range spatiotemporal dependencies under sparse sensor configurations, particularly for high-fidelity, full spatiotemporal reconstruction of time-varying flow fields (e.g., velocity, pressure, temperature). To address this, we propose FNO-Mamba—a novel hybrid architecture that synergistically integrates Fourier Neural Operators (FNOs) for efficient global spatial frequency-domain feature extraction with the Mamba state-space model for scalable, long-horizon temporal dependency modeling. This design overcomes classical trade-offs between computational complexity and reconstruction accuracy in temporal modeling, achieving linear-time inference complexity while preserving high-fidelity reconstruction. Evaluated on multi-scale flow field reconstruction tasks, FNO-Mamba consistently outperforms existing state-of-the-art methods, reducing average prediction error over long sequences by 21.6%. The framework establishes a new, efficient, and scalable paradigm for data-driven physical modeling.

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