Kim Anh Phung | Searcharxiv

arXiv2026

KidRisk: Benchmark Dataset for Children Dangerous Action Recognition

Children are naturally energetic, and during their spontaneous activities, they often encounter potentially dangerous situations, especially when lacking parental supervision. Identifying actions that pose risks plays a crucial role in ensuring their safety. This paper build a novel challenging dataset, namely KidRisk, including 2,500 short videos of children's actions and 10,000 images for dangerous action of children. We also introduce a benchmark on our newly constructs dataset and find that traditional deep learning models demonstrated limited effectiveness on these tasks. Therefore, we develop vision-language based baselines with exceptional context understanding of visual information. Our proposed methods achieved an accuracy of 83.53% in classifying children's actions and 96.14% in recognizing children's dangerous actions, significantly outperforming traditional approaches. These results confirm that vision-language models are not only feasible but also highly effective in detecting hazardous actions, contributing positively to safeguarding children's safety.

cs.CV↗

arXiv2026

PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback

Inpainting, the process of filling missing or corrupted image parts, has broad applications in medical imaging. However, generating anatomically accurate synthetic polyp images for clinical AI is a largely underexplored problem. In specialized fields like gastroenterology, inaccuracies in generated images can lead to false patterns and significant errors in downstream diagnosis. To ensure reliability, models require direct feedback from domain experts like oncologists. We propose PrefPaint, an interactive system that incorporates expert human feedback into Stable Diffusion Inpainting. By using D3PO instead of full RLHF, our approach bypasses the need for computationally expensive reward models, making it a highly practical choice for resource-constrained clinical settings. Furthermore, we introduce a streamlined web-based interface to facilitate this expert-in-the-loop training. Central to this platform is the Model Tree versioning interface, a novel HCI concept that visualizes the evolutionary progression of fine-tuned models. This interactive interface provides a smooth and intuitive user experience, making it easier to offer feedback and manage the fine-tuning process. User studies show that PrefPaint outperforms existing methods, reducing visual inconsistencies and generating highly realistic, anatomically accurate polyp images suitable for clinical AI applications.

cs.CV↗