Sen Xu

EAAI Journal 2025 Journal Article

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

• Qinghui Chen
• LunQian Wang
• Zekai Zhang
• XingHua Wang
• Weilin Liu
• Bo Xia
• Hao Ding
• Jinglin Zhang

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

IROS Conference 2025 Conference Paper

USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways

• Shanliang Yao
• Runwei Guan
• Yi Ni
• Sen Xu
• Yong Yue 0001
• Xiaohui Zhu
• Ryan Wen Liu

AAAI Conference 2024 Conference Paper

Learning Invariant Inter-pixel Correlations for Superpixel Generation

• Sen Xu
• Shikui Wei
• Tao Ruan
• Lixin Liao

Deep superpixel algorithms have made remarkable strides by substituting hand-crafted features with learnable ones. Nevertheless, we observe that existing deep superpixel methods, serving as mid-level representation operations, remain sensitive to the statistical properties (e.g., color distribution, high-level semantics) embedded within the training dataset. Consequently, learnable features exhibit constrained discriminative capability, resulting in unsatisfactory pixel grouping performance, particularly in untrainable application scenarios. To address this issue, we propose the Content Disentangle Superpixel (CDS) algorithm to selectively separate the invariant inter-pixel correlations and statistical properties, i.e., style noise. Specifically, We first construct auxiliary modalities that are homologous to the original RGB image but have substantial stylistic variations. Then, driven by mutual information, we propose the local-grid correlation alignment across modalities to reduce the distribution discrepancy of adaptively selected features and learn invariant inter-pixel correlations. Afterwards, we perform global-style mutual information minimization to enforce the separation of invariant content and train data styles. The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-the-art methods, regarding boundary adherence, generalization, and efficiency. Code and pre-trained model are available at https://github.com/rookiie/CDSpixel.

ECAI Conference 2012 Conference Paper

Disambiguating Road Names in Text Route Descriptions using Exact-All-Hop Shortest Path Algorithm

• Xiao Zhang 0019
• Baojun Qiu
• Prasenjit Mitra
• Sen Xu
• Alexander Klippel
• Alan M. MacEachren

Automatic extraction and understanding of human-generated route descriptions have been critical to research aiming at understanding human cognition of geospatial information. Among all research issues involved, road name disambiguation is the most important, because one road name can refer to more than one road. Compared with traditional toponym (place name) disambiguation, the challenges of disambiguating road names in human-generated route description are three-fold: (1) the authors may use a wrong or obsolete road name and the gazetteer may have incomplete or out-of-date information; (2) geographic ontologies often used to disambiguate cities or counties do not exist for roads, due to their linear nature and large spatial extent; (3) knowledge of the co-occurrence of road names and other toponyms are difficult to learn due to the difficulty in automatic processing of natural language and lack of external information source of road entities. In this paper, we solve the problem of road name disambiguation in human-generated route descriptions with noise, i. e. in the presence of wrong names and incomplete gazetteer. We model the problem as an Exact-All-Hop Shortest Path problem on a semi-complete directed k-partite graph, and design an efficient algorithm to solve it. Our disambiguation algorithm successfully handles the noisy data and does not require any extra information sources other than the gazetteer. We compared our algorithm with an existing map-based method. Experiment results show that our algorithm significantly outperforms the existing method.