Author name cluster

Qifei Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

JBHI Journal 2026 Journal Article

Wavelet-Driven Spatial Frequency Mamba Network for Spine Image Segmentation

Yuefeng Zhao
Qifei Wang
Nai Zhou
Pengfei Sun
Junjie Wang
Nannan Hu

Accurate spine segmentation is crucial for diagnosing and treating various spine diseases. Recently, Mamba-based methods have been widely applied in medical image segmentation. However, the spatial domain scanning strategy of Mamba fails to fully capture the fine anatomical structures and global dependencies of the spine. Moreover, existing frequency-enhanced methods often suffer from the loss of spatial localization. To address these challenges, we propose the Wavelet-Driven Spatial Frequency Mamba Network (WDSFM-Net). Specifically, we integrate the Discrete Wavelet Transform (DWT) into Mamba to construct the Spatial-Frequency Mamba Block (SFMB). By decomposing features into distinct frequency subbands, SFMB explicitly captures global structural context from low-frequency components while enhancing local anatomical details through high-frequency components. To accommodate specific spinal morphology, the Global Strip Pooling Attention (GSPA) module aggregates directional contexts to model the elongated and anisotropic spinal anatomy, while the Multi-Scale Attention Enhancement (MSAE) module employs multi-scale convolutions to adapt to significant vertebral scale variations. Additionally, we introduce a Dual-Domain Loss (DDL) function, which optimizes both spatial and frequency domain representations for robust training. We evaluated our WDSFM-Net on two public spine MRI datasets. The results show that the WDSFM-Net outperforms other state-of-the-art methods, achieving average Dice similarity coefficients of 0. 8885 and 0. 8669 in the Spider and MRSpine datasets, respectively.

Details DOI

IROS Conference 2025 Conference Paper

View-aware Decomposition and Unification for Fast Ground-to-Aerial Person Search

Qifei Wang
Pengcheng Zhang 0003
Xiaohan Yu 0001
Xiao Bai 0001
Yongsheng Gao 0001

Ground-to-aerial person search leverages cooperative efforts between unmanned aerial vehicles (UAV) and ground surveillance cameras to locate person individuals. Despite the progress made by recent works, the impact of the discrepancy between the two views is underestimated. This limits the overall person search performance when training the model in a view-agnostic way. To address this, we propose a view-aware decomposition and unification (VADU) framework for ground-to-aerial person search. Specifically, we decompose the person search model to learn view-oriented modules for image feature encoding and person proposal generation. The data sampling and retrieval feature learning are also composed to cope with the decomposed model. This decomposition improves both person detection and discriminative feature learning within each view. On top of the decomposition, we propose view-aware unification to produce unified cross-view person features. Cross-view prototypical contrastive learning is introduced to enhance the unification between different views, enhancing model robustness to retrieve a target person in cameras of a different view. As the decomposed parts of the model are deployed on different devices for inference, this overall framework adds no extra computation cost in real-world applications. Extensive experiments demonstrate that the proposed method achieves superior person search performance and guarantees the efficiency of inference. The source code is available at https://github.com/QFWang-11/vadu.

Details

NeurIPS Conference 2024 Conference Paper

Optical Diffusion Models for Image Generation

Ilker Oguz
Niyazi U. Dinc
Mustafa Yildirim
Junjie Ke
Innfarn Yoo
Qifei Wang
Feng Yang
Christophe Moser

Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware such as GPUs. In this study, we demonstrate that the propagation of a light beam through a transparent medium can be programmed to implement a denoising diffusion model on image samples. This framework projects noisy image patterns through passive diffractive optical layers, which collectively only transmit the predicted noise term in the image. The optical transparent layers, which are trained with an online training approach, backpropagating the error to the analytical model of the system, are passive and kept the same across different steps of denoising. Hence this method enables high-speed image generation with minimal power consumption, benefiting from the bandwidth and energy efficiency of optical information processing.

PDF Details DOI