Abstract
Current state-of-the-art methods for object detection rely on annotated bounding boxes of large data sets for training. However, obtaining such annotations is expensive and can require up to hundreds of hours of manual labor. This poses a challenge, especially since such annotations can only be provided by experts, as they require knowledge about the scientific domain. To tackle this challenge, we propose a domain-specific weakly supervised object detection algorithm that only relies on image-level annotations, which are significantly easier to acquire. Our method distills the knowledge of a pre-trained model, on the task of predicting the presence or absence of a virus in an image, to obtain a set of pseudo-labels that can be used to later train a state-of-the-art object detection model. To do so, we use an optimization approach with a shrinking receptive field to extract virus particles directly without specific network architectures. Through a set of extensive studies, we show how the proposed pseudo-labels are easier to obtain, and, more importantly, are able to outperform other existing weak labeling methods, and even ground truth labels, in cases where the time to obtain the annotation is limited.
Abstract (translated)
当前最先进的物体检测方法依赖于大型数据集上的标注边界框进行训练。然而，获取这些注释非常昂贵，并且可能需要数百小时的人工劳动。这一挑战尤其严重，因为这类注释只能由具备特定科学领域知识的专家提供。为了解决这个问题，我们提出了一种基于图像级标签的领域特定弱监督物体检测算法，这种标签更容易获得。我们的方法通过一个预训练模型的知识蒸馏过程，在预测图像中是否存在病毒的任务上获取一组伪标签，这些伪标签可以用于后续训练最先进的物体检测模型。为此，我们采用了一个优化方法，并使用了收缩感受野的方法来直接提取病毒颗粒，而无需特定的网络架构。通过对一系列广泛的实验研究显示，所提出的伪标签更容易获得，并且在注释时间有限的情况下，能够优于其他现有的弱标注方法，甚至可以超越真实的地面实况标签。
URL
https://arxiv.org/abs/2508.00563
