Research shows a noticeable drop in performance of object detectors when the training data has missing annotations, i.e. sparsely annotated data. Contemporary methods focus on proxies for missing ground-truth annotations either in the form of pseudo-labels or by re-weighing gradients for unlabeled boxes during training. In this work, we revisit the formulation of sparsely annotated object detection. We observe that sparsely annotated object detection can be considered a semi-supervised object detection problem at a region level. Building on this insight, we propose a region-based semi-supervised algorithm, that automatically identifies regions containing unlabeled foreground objects. Our algorithm then processes the labeled and un-labeled foreground regions differently, a common practice in semi-supervised methods. To evaluate the effectiveness of the proposed approach, we conduct exhaustive experiments on five splits commonly used by sparsely annotated approaches on the PASCAL-VOC and COCO datasets and achieve state-of-the-art performance. In addition to this, we show that our approach achieves competitive performance on standard semi-supervised setups demonstrating the strength and broad applicability of our approach.
翻译:当培训数据缺少说明时,即附加说明的数据极少,物体探测器的性能明显下降。当代方法侧重于缺失地面实况说明的代理人,无论是假标签形式,还是培训期间未贴标签箱的重度梯度。在这项工作中,我们重新审视了未贴标签箱的微度梯度。我们观察到,少量附加说明的物体探测可被视为一个区域级的半监督对象探测问题。基于这一洞察,我们提议以区域为基础的半监督算法,该算法自动识别含有未贴标签的地面物体的区域。我们的算法随后以不同的方式处理标签和未贴标签的地面说明区域,这是半监督方法中的一种常见做法。为了评估拟议方法的有效性,我们在五处常见的分割上进行了详尽的实验,对PACAL-VOC和COCO数据集的标记方法很少加注解,并实现了最先进的性能。除此之外,我们还展示了我们在标准的半监督设置方法上具有竞争性的表现,展示了我们的实力和广泛适用性。