Outputs (5)

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective (2025)
Journal Article
Shao, M., Wang, Z., Duan, H., Huang, Y., Zhai, B., Wang, S., Long, Y., & Zheng, Y. (online). Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective. IEEE Transactions on Medical Imaging, https://doi.org/10.1109/tmi.2025.3579213

Precise segmentation of brain tumors, particularly contrast-enhancing regions visible in post-contrast MRI (areas highlighted by contrast agent injection), is crucial for accurate clinical diagnosis and treatment planning but remains challenging. How... Read More about Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective.

Towards stereoscopic vision: Attention-guided gaze estimation with EEG in 3D space (2025)
Journal Article
Qin, D., Long, Y., Zhang, X., Zhou, Z., Jin, Y., & Wang, P. (2025). Towards stereoscopic vision: Attention-guided gaze estimation with EEG in 3D space. Neurocomputing, 648, Article 130577. https://doi.org/10.1016/j.neucom.2025.130577

Since traditional gaze-tracking methods rely on line-of-sight estimation, spatial attention modeling from neural activity offers an alternative perspective to gaze estimation. This paper presents a proof-of-concept study on attenti... Read More about Towards stereoscopic vision: Attention-guided gaze estimation with EEG in 3D space.

FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off (2025)
Journal Article
Zhang, T., Wan, F., Duan, H., Tong, K. W., Deng, J., & Long, Y. (2025). FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off. Knowledge-Based Systems, 317, Article 113393. https://doi.org/10.1016/j.knosys.2025.113393

Spatial convolution is fundamental in constructing deep Convolutional Neural Networks (CNNs) for visual recognition. While dynamic convolution enhances model accuracy by adaptively combining static kernels, it incurs significant computational overhe... Read More about FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off.

Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields (2025)
Journal Article
Miao, X., Duan, H., Bai, Y., Shah, T., Song, J., Long, Y., Ranjan, R., & Shao, L. (2025). Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5), 3922-3934. https://doi.org/10.1109/TPAMI.2025.3535916

In this work, we propose a method that leverages CLIP feature distillation, achieving efficient 3D segmentation through language guidance. Unlike previous methods that rely on multi-scale CLIP features and are limited by processing speed and storage... Read More about Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields.

The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces (2025)
Journal Article
Ma, W., Ma, T., Organisciak, D., Waide, J. E. T., Meng, X., & Long, Y. (2025). The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces. Electronics, 14(3), Article 508. https://doi.org/10.3390/electronics14030508

The vigorous development of deep learning (DL) has been propelled by big data and high-performance computing. For brain–computer interfaces (BCIs) to benefit from DL in a reliable and scalable manner, the scale and quality of data are crucial. Specia... Read More about The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces.