论文标题
半杂质：半监督的多动姿势估计框架
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework
论文作者
论文摘要
多动物姿势估计对于研究动物在神经科学和神经障碍中的社会行为至关重要。已经提出了先进的方法来支持多动画估计并实现最先进的绩效。但是，这些模型在培训期间很少利用未标记的数据，即使现实世界应用比标记的框架更为未标记的框架。手动为大量图像或视频添加密集注释是昂贵且劳动密集型的，尤其是对于多个实例。鉴于这些缺陷，我们提出了一种新型的半监督结构，以进行多动画姿势估计，利用行为视频中未标记的框架中广泛的结构来增强训练，这对于稀疏标记的问题至关重要。与最先进的基线相比，所得算法将为三个动物实验提供出色的多动物姿势估计结果，并在稀疏标记的数据方案中表现出更具预测性的能力。
Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology. Advanced approaches have been proposed to support multi-animal estimation and achieve state-of-the-art performance. However, these models rarely exploit unlabeled data during training even though real world applications have exponentially more unlabeled frames than labeled frames. Manually adding dense annotations for a large number of images or videos is costly and labor-intensive, especially for multiple instances. Given these deficiencies, we propose a novel semi-supervised architecture for multi-animal pose estimation, leveraging the abundant structures pervasive in unlabeled frames in behavior videos to enhance training, which is critical for sparsely-labeled problems. The resulting algorithm will provide superior multi-animal pose estimation results on three animal experiments compared to the state-of-the-art baseline and exhibits more predictive power in sparsely-labeled data regimes.
