Outputs (61)

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.

Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination (2024)
Journal Article
Jiang, C., Wang, S., Long, Y., Li, Z., Zhang, H., & Shao, L. (2025). Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(3), 1395-1413. https://doi.org/10.1109/tpami.2024.3487631

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of seen primitives. Prior studies have attempted to either learn primitives individually (non-connected) or establish dependencies among them in the composition (fully-conne... Read More about Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination.

SID-NERF: Few-Shot Nerf Based on Scene Information Distribution (2024)
Presentation / Conference Contribution
Li, Y., Wan, F., & Long, Y. (2024, July). SID-NERF: Few-Shot Nerf Based on Scene Information Distribution. Presented at 2024 IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada

The novel view synthesis from a limited set of images is a significant research focus. Traditional NeRF methods, relying mainly on color supervision, struggle with accurate scene geometry reconstruction when faced with sparse input images, leading to... Read More about SID-NERF: Few-Shot Nerf Based on Scene Information Distribution.

CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video (2024)
Journal Article
Miao, X., Bai, Y., Duan, H., Wan, F., Huang, Y., Long, Y., & Zheng, Y. (2024). CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video. Pattern Recognition, 156, Article 110729. https://doi.org/10.1016/j.patcog.2024.110729

The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown impressive performance by leveraging time-varying dynamic radiation fields. However, thes... Read More about CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video.

Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning (2024)
Journal Article
Liu, X., Wang, C., Yang, G., Wang, C., Long, Y., Liu, J., & Zhang, Z. (2024). Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning. Electronics, 13(10), Article 1977. https://doi.org/10.3390/electronics13101977

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on sema... Read More about Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning.

Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval (2024)
Journal Article
Ma, T., Organisciak, D., Ma, W., & Long, Y. (2024). Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval. Electronics, 13(9), Article 1660. https://doi.org/10.3390/electronics13091660

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and... Read More about Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval.