2025 ICCV ICCV 2025

PossLoss: A Reliable and Sensitive Facial Landmark Detection Loss Function

Abstract

ate and locate the peak of the heatmap, while adaptively balancing the influence of landmarks and background pixels through self-weighting, addressing the extreme imbalance between landmarks and non-landmarks. More advanced is that our PossLoss is sample-sensitive, which can distinguish easy and hard landmarks and adaptively make the model focused more on hard landmarks. Moreover, it addresses the difficulty of accurately evaluating heatmap distribution, especially in addressing small errors due to peak mismatches. We analyzed and evaluated our PossLoss on three challenging facial landmark detection tasks. The experimental results show that our PossLoss significantly improves the performance of landmark detection and outperforms the state-of-the-art methods.

🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors