Duc Tai Phan

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2026

1. KBS

   

   From object difficulty to image scoring: A strategy for active learning in object detection

   Duc Tai Phan , Nhut Minh Nguyen , Khang Phuc Nguyen , Phuong-Nam Tran , Nhat Truong Pham , Linh Le , Choong Seon Hong , and Duc Ngoc Minh Dang

   Knowledge-Based Systems, Apr 2026

   Abs Bib HTML Code

   Object detection (OD) faces the costly hurdle of large-scale dataset annotation. Active Learning (AL) is a promising solution that selects the most beneficial samples for annotation. Applying AL to OD is challenging, as it requires addressing uncertainties in both classifying and localizing objects while combining object-level information into a single image-level decision. Current methods, like uncertainty sampling, often rely on output-level uncertainty or heuristic signal combinations. However, relying on static model outputs limits their ability to capture underlying feature-space instabilities. To address these limitations, we propose Feature Difficulty-based AL (FDAL), a novel framework that shifts the focus from output-level heuristics to active latent-space manipulation. By systematically interpolating unlabeled features toward class anchors, FDAL identifies prediction inconsistencies to reveal hidden traits unknown to the model. This anchor-guided interpolation unifies classification and localization uncertainties, capturing latent instabilities that traditional paradigms miss. Experiments on four OD benchmarks demonstrate that FDAL achieves state-of-the-art (SOTA) results, consistently improving detection accuracy while substantially reducing annotation costs. FDAL outperforms SOTA methods by 0.8% in detection accuracy on Pattern analysis, statistical modelling, and computational learning (PASCAL) Visual Object Classes (PASCAL VOC), 1.99% on Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), 1.68% on Cityscapes, and 0.27% on Microsoft Common Objects in Context (MS COCO). FDAL’s selection is highly efficient, taking 0.06 seconds per image on PASCAL VOC and up to 0.63 seconds per image on Cityscapes, enabling scalability to large unlabeled pools. FDAL offers a practical, efficient, and effective solution for advancing AL in OD.

   @article{PHAN2026115946,
     author = {Phan, Duc Tai and Nguyen, Nhut Minh and Nguyen, Khang Phuc and Tran, Phuong-Nam and Pham, Nhat Truong and Le, Linh and Hong, Choong Seon and Dang, Duc Ngoc Minh},
     title = {From object difficulty to image scoring: A strategy for active learning in object detection},
     journal = {Knowledge-Based Systems},
     month = apr,
     year = {2026},
     keywords = {Active learning, Object detection, Computer vision, Difficulty, Instability},
     volume = {342},
     pages = {115946},
     issn = {0950-7051},
     doi = {https://doi.org/10.1016/j.knosys.2026.115946},
     url = {https://www.sciencedirect.com/science/article/pii/S0950705126006726}
   }

2025

1. MEDES’25

   

   DAAL: Dual Ambiguity in Active Learning for Object Detection with YOLOE

   Duc Tai Phan , Nhut Minh Nguyen , and Duc Ngoc Minh Dang

   In 17th International Conference on Management of Digital Ecosystems , Ho Chi Minh City, Vietnam, Nov 2025

   Abs Code

   The high cost of annotating vast datasets poses a significant challenge in object detection, hindering the development of robust models. By selectively annotating the most instructive samples, active learning has become a viable strategy for addressing this problem and improving model performance and labeling efficiency. However, the two main types of model uncertainty—epistemic, which captures the model’s uncertainty in classifying an object, and aleatoric, which addresses the inherent ambiguity in an object’s presence and location—are frequently not balanced in traditional active learning techniques. In this paper, we introduce DAAL (Dual Ambiguity Active Learning), a novel framework that quantifies and combines both epistemic and aleatoric ambiguity into a single, weighted score. Epistemic ambiguity measures the model’s indecision in assigning semantic labels, while aleatoric ambiguity assesses the conviction in object presence and localization. By combining these, DAAL selects the most informative images for annotation, optimizing model performance under limited labeling budgets. Extensive experiments on popular benchmarks demonstrate that DAAL consistently outperforms traditional methods, achieving superior accuracy under the same limited labeling budget. This affirms its effectiveness in creating more efficient annotation workflows for object detection.

2. APNOMS’25

   

   ALMUS: Enhancing Active Learning for Object Detection with Metric-Based Uncertainty Sampling

   Duc Tai Phan , Nhut Minh Nguyen , Khang Phuc Nguyen , Tri Minh Pham , and Duc Ngoc Minh Dang

   In 2025 25th Asia-Pacific Network Operations and Management Symposium (APNOMS’2025) , Kaohsiung, Taiwan, Sep 2025

   Abs Bib HTML

   Object detection is critical in computer vision but often requires large amounts of labeled data for effective training. Active learning (AL) has emerged as a promising solution to reduce the annotation burden by selecting the most informative samples for labeling. However, existing AL methods for object detection primarily focus on uncertainty sampling, which may not effectively balance the dual challenges of classification and localization. In this study, we explore active learning for object detection, with the objective of optimizing model performance while substantially reducing the demand for annotated data. We propose a novel Active Learning with Metric-based Uncertainty Sampling (ALMUS) that works effectively for the object detection task. This approach prioritizes selecting images containing objects from categories where the model exhibits suboptimal performance, as determined by category-specific evaluation metrics. To balance the annotation budget across different object classes, we propose a dynamic allocation strategy that considers the difficulty of each class and the distribution of object instances within the dataset. This combination of strategies enables our method to effectively address the dual challenges of classification and localization in object detection tasks while still focusing on the rarest and most challenging classes. We conduct extensive experiments on the PASCAL VOC 2007 and 2012 datasets, demonstrating that our method outperforms several active learning baselines. Our results indicate that the proposed approach enhances model performance and accelerates convergence, making it a valuable contribution to the field of active learning in object detection.

   @inproceedings{11181447,
     author = {Phan, Duc Tai and Nguyen, Nhut Minh and Nguyen, Khang Phuc and Pham, Tri Minh and Dang, Duc Ngoc Minh},
     title = {ALMUS: Enhancing Active Learning for Object Detection with Metric-Based Uncertainty Sampling},
     booktitle = {2025 25th Asia-Pacific Network Operations and Management Symposium (APNOMS'2025)},
     address = {Kaohsiung, Taiwan},
     pages = {6},
     month = sep,
     year = {2025},
     keywords = {Location awareness;Measurement;Training;Uncertainty;Annotations;Active learning;Object detection;Robustness;Resource management;Convergence;Active Learning;Object Detection;Uncertainty Sampling;Metric-based Sampling},
     doi = {10.23919/APNOMS67058.2025.11181447},
     isbn = {978-1-23456-789-0},
   }

2024

1. RIVF’24

   

   Improving Face Attendance Checking System with Ensemble Learning

   Duc Tai Phan , Phuong-Nam Tran , and Duc Ngoc Minh Dang

   In 2024 RIVF International Conference on Computing and Communication Technologies (RIVF 2024) , Danang, Vietnam, Dec 2024

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   An accurate and efficient attendance system is essential in modern industrial and educational settings. However, traditional methods often suffer from inaccuracies, inefficiencies, and vulnerability to fraud. Recent work usually utilizes a single model for employing the face recognition system, leading to many failures due to the model’s low performance. This paper presents an advanced face attendance system using ensemble learning to overcome the limitations of single-model approaches. The proposed system achieves near-perfect accuracy under optimal conditions by integrating the strengths of multiple deep learning models, including ResNet, VGGFace, and FaceNet. The ensemble approach boosts the robustness and reliability of the attendance system, making it a promising solution for real-world deployment in educational and workplace environments. The key contribution of this work is the development of a face attendance system that utilizes the complementary capabilities of different models to deliver significantly improved accuracy and resilience compared to standalone methods.

   @inproceedings{11009085,
     author = {Phan, Duc Tai and Tran, Phuong-Nam and Dang, Duc Ngoc Minh},
     title = {Improving Face Attendance Checking System with Ensemble Learning},
     booktitle = {2024 RIVF International Conference on Computing and Communication Technologies (RIVF 2024)},
     address = {Danang, Vietnam},
     pages = {5},
     month = dec,
     year = {2024},
     doi = {10.1109/RIVF64335.2024.11009085},
     isbn = {979-8-3315-0507-3},
   }