Xiaosong Li

AAAI Conference 2026 Conference Paper

Text-Guided Channel Perturbation and Pre-Trained Knowledge Integration for Unified Multi-Modality Image Fusion

• Xilai Li
• Xiaosong Li
• Weijun Jiang

Multi-modality image fusion enhances scene perception by combining complementary information. Unified models aim to share parameters across modalities for multi-modality image fusion, but large modality differences often cause gradient conflicts, limiting performance. Some methods introduce modality-specific encoders to enhance feature perception and improve fusion quality. However, this strategy reduces generalisation across different fusion tasks. To overcome this limitation, we propose a unified multi-modality image fusion framework based on channel perturbation and pre-trained knowledge integration (UP-Fusion). To suppress redundant modal information and emphasize key features, we propose the Semantic-Aware Channel Pruning Module (SCPM), which leverages the semantic perception capability of a pre-trained model to filter and enhance multi-modality feature channels. Furthermore, we proposed the Geometric Affine Modulation Module (GAM), which uses original modal features to apply affine transformations on initial fusion features to maintain the feature encoder modal discriminability. Finally, we apply a Text-Guided Channel Perturbation Module (TCPM) during decoding to reshape the channel distribution, reducing the dependence on modality-specific channels. Extensive experiments demonstrate that the proposed algorithm outperforms existing methods on both multi-modality image fusion and downstream tasks.

EAAI Journal 2025 Journal Article

Advancing the safety of intelligent rail transit systems: A segmentation network for efficient end-of-track degradation feature extraction

• Tao Ye
• Haoran Chen
• Guopeng Liu
• Liu Liu
• Hongbin Ren
• Xiaosong Li
• Xi Zhang

Accurate segmentation of rail track is crucial for the safe autonomous driving of intelligent trains. Current train operations struggle with insufficient precision in rail track segmentation, primarily due to poor end-of-track segmentation performance caused by degradation of track-end features. To address these challenges, we propose Rail Track End Wise Network (RTEW-Net), an effective rail track end wise segmentation method. This network utilizes Full-Transformer Module (FTM) for effective track feature extraction and integrates the Global Response Normalization (GRN) module to handle drastic lighting changes. Additionally, we designed the Wise Weigh Maintain (WWM) method to enhance feature learning and retain track features. To validate its effectiveness, we constructed the RailMixed2024 (RM2024) dataset. Our model achieves high-precision global rail track segmentation and optimizes end detection. Experimental results demonstrate that RTEW-Net exhibits outstanding performance on the RM2024 and RailSem19 datasets, establishing it as the state-of-the-art (SOTA) in this field.

EAAI Journal 2025 Journal Article

Erratum to “Advancing the safety of intelligent rail transit systems: A segmentation network for efficient end-of-track degradation feature extraction” [Eng. Appl. Artif. Intellig. 159 (2025) 111527]

• Tao Ye
• Haoran Chen
• Guopeng Liu
• Liu Liu
• Hongbin Ren
• Xiaosong Li
• Xi Zhang

EAAI Journal 2024 Journal Article

Explicitly learning augmentation invariance for image classification by Consistent Augmentation

• Xiaosong Li
• Yanxia Wu
• Chuheng Tang
• Yan Fu
• Lidan Zhang

Data augmentation is a powerful and widely used technique to improve the generalization of convolutional neural networks. The majority of data augmentation methods emphasize transforming samples to bolster network robustness. However, these methods overlook learning the invariance between the samples augmented by different transformations, impairing the quality of learned representations and generalization of the model. In this paper, we introduce a straightforward yet highly effective method called Consistent Augmentation (CA) to compel networks to learn explicitly the invariance of transformations. Specifically, CA minimizes the bidirectional KL divergence between the predicted distributions of different variants generated from the same samples using different transformations, explicitly enforcing consistency among various semantics-preserving transformations. Implementing CA is intuitive, uncomplicated, and does not introduce additional computational costs during inference. The validation experiments performed on conventional and fine-grained classification tasks demonstrate that CA can be employed across various network architectures and consistently improve their generalization ability. Notably, convolutional neural networks benefit significantly on fine-grained classification tasks, with an improvement of 5. 18% and 6. 61% compared with other augmentation methods in accuracy observed for the ShuffleNetV2 model on the Stanford Dogs and CUB-200-2011 datasets. Compatibility experiments conducted on the CIFAR-100 dataset reveal that CA improves the accuracy of the Mixup and CutMix methods by 1. 25% and 2. 11%, respectively. These results underscore the effectiveness of our approach when employed alongside mix-based augmentation methods, further enhancing the capabilities of the model.

JBHI Journal 2024 Journal Article

Generative Adversarial Network for Trimodal Medical Image Fusion Using Primitive Relationship Reasoning

• Jingxue Huang
• Xiaosong Li
• Haishu Tan
• Xiaoqi Cheng

Medical image fusion has become a hot biomedical image processing technology in recent years. The technology coalesces useful information from different modal medical images onto an informative single fused image to provide reasonable and effective medical assistance. Currently, research has mainly focused on dual-modal medical image fusion, and little attention has been paid on trimodal medical image fusion, which has greater application requirements and clinical significance. For this, the study proposes an end-to-end generative adversarial network for trimodal medical image fusion. Utilizing a multi-scale squeeze and excitation reasoning attention network, the proposed method generates an energy map for each source image, facilitating efficient trimodal medical image fusion under the guidance of an energy ratio fusion strategy. To obtain the global semantic information, we introduced squeeze and excitation reasoning attention blocks and enhanced the global feature by primitive relationship reasoning. Through extensive fusion experiments, we demonstrate that our method yields superior visual results and objective evaluation metric scores compared to state-of-the-art fusion methods. Furthermore, the proposed method also obtained the best accuracy in the glioma segmentation experiment.