publications

2025

1. 

   PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs

   Zhilin Zhang, Xiang Zhang, Jiaqi Wei, and 2 more authors

   2025

   PDF
2. IJCNN 2025

   

   Enhancing Intra-Modality Compactness in Text-to-Image Person ReID

   Zhanghao Qin, Hongming Zhang, Zhilin Zhang, and 3 more authors

   Accepted by IJCNN 2025, 2025
3. CVCI 2025

   

   Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach

   Zhilin Zhang, and Fangyu Wu

   In Proceedings of the 2025 6th International Conference on Computer Vision and Computational Intelligence, 2025

   Abs DOI Bib PDF

   Visual Question Answering (VQA) has emerged as a highly engaging field in recent years, with increasing research focused on enhancing VQA accuracy through advanced models such as Transformers. Despite this growing interest, limited work has examined the comparative effectiveness of textual encoders in VQA, particularly considering model complexity and computational efficiency. In this work, we conduct a comprehensive comparison between complex textual models that leverage long-range dependencies and simpler models focusing on local textual features within a well-established VQA framework. Our findings reveal that employing complex textual encoders is not always the optimal approach for the VQA-v2 dataset. Motivated by this insight, we propose ConvGRU, a model that incorporates convolutional layers to improve text feature representation without substantially increasing model complexity. Tested on the VQA-v2 dataset, ConvGRU demonstrates a modest yet consistent improvement over baselines for question types such as Number and Count, which highlights the potential of lightweight architectures for VQA tasks, especially when computational resources are limited.

   @inproceedings{10.1145/3744725.3744731,
     author = {Zhang, Zhilin and Wu, Fangyu},
     title = {Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach},
     year = {2025},
     isbn = {9798400712920},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3744725.3744731},
     doi = {10.1145/3744725.3744731},
     booktitle = {Proceedings of the 2025 6th International Conference on Computer Vision and Computational Intelligence},
     pages = {42–50},
     numpages = {9},
     keywords = {Convolution, N-Grams, Textual Feature Extraction, Visual Question Answering},
     series = {CVCI '25}
   }

2024

1. 

   SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment

   Jie Wang, Yichen Wang, Zhilin Zhang, and 3 more authors

   Under Review, 2024

   PDF

   
2. IJCNN 2025

   

   Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering

   Zhilin Zhang, Jie Wang, Zhanghao Qin, and 2 more authors

   Accepted by IJCNN 2025, 2024

   PDF