Publications
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DiffRGD: A Training-Free Diffusion Guidance Through Riemannian Gradient Descent
In submission
M-ErasureBench: A Comprehensive Multimodal Evaluation Benchmark for Concept Erasure in Diffusion Models
WACV 2026
TL;DR
M-ErasureBench is a multimodal benchmark revealing that existing diffusion concept-erasure methods fail beyond text prompts and introduces an inference-time module that significantly improves erasure robustness without retraining.
Zero-shot Geometry-Aware Diffusion Guidance for Music Restoration
NeurIPS 2025 AI4Music Workshop
TL;DR
Diffusion Geodesic Guidance (DGG) is a zero-shot geometry-aware diffusion guidance method that updates samples along hyperspherical geodesics to preserve the model prior while improving music restoration quality without retraining.
BEVAN: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation
ICIP 2025
TL;DR
BEVANet is a bilateral large-kernel attention network that achieves state-of-the-art real-time semantic segmentation by adaptively expanding receptive fields and fusing semantic, structural, and boundary features efficiently.
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative Refinement
WACV 2025
TL;DR
DiffQRCoder is a training-free diffusion framework that generates visually appealing QR codes while preserving high scanning robustness through geometry-aware perceptual guidance and iterative refinement.
Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models
AAAI 2025
TL;DR
AtkPDM is a feature-space adversarial attack framework that crafts imperceptible perturbations to protect images from unauthorized diffusion-based editing by disrupting UNet representations while preserving visual fidelity.
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
CoRL 2024
TL;DR
DDFH is an active learning framework for LiDAR-based 3D object detection that selects the most informative samples by jointly modeling distribution discrepancy and feature heterogeneity to reduce annotation cost while improving detection performance.
An UNet-Based Brain Tumor Segmentation Framework via Optimal Mass Transportation Pre-processing
MICCAI 2022 Brainlesion Workshop
TL;DR
This paper proposes a two-phase UNet-based brain tumor segmentation framework that uses optimal mass transportation to enlarge tumor regions and enhance data diversity, significantly improving MRI segmentation accuracy and robustness.