Jian Nong | Computer Vision and Image Processing | Best Researcher Award

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Prof. Dr. Jian Nong | Computer Vision and Image Processing | Best Researcher Award

Associate Professor at Wuzhou University | China

Prof. Dr. Jian Nong is a distinguished Associate Professor at the School of Artificial Intelligence, Wuzhou University, with expertise spanning computer vision, deep learning, and high-performance computing. He earned his Ph.D. in Computer Technology and Its Applications from the Macau University of Science and Technology, where he cultivated a strong research foundation in intelligent computing and visual information analysis. His academic pursuits center on visual object tracking, multi-modal data fusion, sentiment analysis, and GPU-based parallel processing. Prof. Dr. Nong has authored several influential papers in reputed international journals and conferences, including “Robust Tracking via Rethinking Prediction Head” (Image and Vision Computing, 2025), “Dual-stream Multi-modal Interactive Vision-language Tracking” (ACM, 2024), “SentiRank: A Novel Approach to Sentiment Leader Identification in Social Networks Based on the D-TFRank Model” (Electronics, 2025), and “Efficient Parallel Processing of R-Tree on GPUs” (Mathematics, 2024). His research outcomes contribute substantially to advancing intelligent vision systems, data-driven decision-making, and high-efficiency computing architectures. As the head of a research and teaching team supporting the Digital Xijiang River Project, he integrates academic research with applied innovation to address regional and industrial digitalization challenges. His ongoing research projects include the development of deep reinforcement learning-based recommendation methods for multi-objective optimization in complex shipping environments and object tracking algorithms leveraging multi-cue information. With over a decade of experience and more than ten scholarly publications, Prof. Dr. Jian Nong continues to play a pivotal role in bridging artificial intelligence theory and practical application, fostering the next generation of intelligent computing systems and contributing to the growth of AI-driven technologies on a global scale.

Profile: Orcid

Featured Publications

  1. Nong, J., Qi, Y., Mo, Z., Wang, J., & Liang, Y. (2025). Robust tracking via rethinking prediction head. Image and Vision Computing, 152, 105780.

  2. Huang, J., Lan, B., Nong, J., Pang, G., & Hao, F. (2025). SentiRank: A novel approach to sentiment leader identification in social networks based on the D-TFRank model. Electronics, 14(14), 2751.

  3. Mo, Z., Zhang, G., Nong, J., Zhong, B., & Li, Z. (2024, December 3). Dual-stream multi-modal interactive vision-language tracking. In Proceedings of the ACM Conference.

  4. Nong, J., He, X., Chen, J., & Liang, Y. (2024). Efficient parallel processing of R-Tree on GPUs. Mathematics, 12(13), 2115.

Dhruv Sharma | Computer Vision | Best Researcher Award

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Dr. Dhruv Sharma | Computer Vision | Best Researcher Award

Amity University | India

Dr. Dhruv Sharma has made extensive contributions to the domains of artificial intelligence, deep learning, and multimodal systems through a wide range of impactful publications. His research encompasses visual data captioning, adaptive attention mechanisms, and transformer-based models that enhance image understanding and description generation. Notable works include Evolution of Visual Data Captioning Methods, Datasets, and Evaluation Metrics: A Comprehensive Survey, Automated Image Caption Generation Framework using Adaptive Attention and Bi-LSTM, and XGL-T Transformer Model for Intelligent Image Captioning, which collectively advance the field of vision-language integration. His studies such as Lightweight Transformer with GRU Integrated Decoder for Image Captioning and Control With Style: Style Embedding-based Variational Autoencoder for Controlled Stylized Caption Generation Framework propose innovative architectures for stylistic and efficient captioning. In addition, he has developed frameworks like FDT–Dr2T: A Unified Dense Radiology Report Generation Transformer Framework for X-ray Images and Unma-Capsumt: Unified and Multi-Head Attention-Driven Caption Summarization Transformer, highlighting his interest in medical AI and caption summarization. His earlier works, including Memory-Based FIR Digital Filter using Modified OMS-LUT Design and Modified Efficient OMS LUT-Design for Memory-Based Multiplication, show his foundational expertise in signal processing and hardware-efficient algorithms. Moreover, his contributions such as Obscenity Detection Transformer and DVRGNet reflect his commitment to developing socially responsible AI for content moderation. Overall, Dr. Sharma’s scholarly output demonstrates a consistent trajectory from traditional signal processing to cutting-edge multimodal AI, bridging research innovation with practical applications in intelligent computing and human-centered artificial intelligence.

Profile: Google Scholar

Featured Publications

  • Sharma, D., Dhiman, C., & Kumar, D. (2023). Evolution of visual data captioning methods, datasets, and evaluation metrics: A comprehensive survey. Expert Systems with Applications, 221, 119773.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). XGL-T transformer model for intelligent image captioning. Multimedia Tools and Applications, 83(2), 4219–4240.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). Control with style: Style embedding-based variational autoencoder for controlled stylized caption generation framework. IEEE Transactions on Cognitive and Developmental Systems, 1–11.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). FDT–Dr2T: A unified dense radiology report generation transformer framework for X-ray images. Machine Vision and Applications, 35, 1–13.

  • Sharma, D., Dhiman, C., & Kumar, D. (2022). Automated image caption generation framework using adaptive attention and Bi-LSTM. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1–5). IEEE.