Learning with incomplete labels in Neural Networks has been actively investigated these last years. Among different kinds of incomplete labels, we investigate incomplete pixel-level labels which are tackled in many concrete problems. One of the challenges for incomplete pixel-level labels is the missing information at local-level. Most of the current researches with incomplete labels in Neural Network focus on the incompleteness of global labels, only a few works focus on the incompleteness of local labels. To deal with the local incompleteness, we propose a learning approach which uses two dynamic weighted maps in parallel: one for object pixels and another one for background pixels. The two maps are integrated into the loss function of the target Neural Networks, to optimize the model by the present labels and to minimize the damage of the missing labels. We validate our approach on the speech balloon extraction problem in comic book images. Our approach uses the output of a balloon extraction algorithm as incomplete labels. The results are comparable with the state of the art supervised approach with manual labels. The results are very promising because our method does not require any manual labels. In addition, we apply our method to the medical image segmentation task to confirm the generalization of our approach.

近年来，在神经网络中使用不完整标签进行学习已得到积极调查。在不同种类的不完整标签中，我们研究了许多具体问题解决的不完整像素级标签。不完整的像素级标签的挑战之一是本地级信息的丢失。目前在神经网络中对不完整标签的研究大多集中在全局标签的不完整上，只有少数研究集中在局部标签的不完整上。为了解决局部不完整问题，我们提出了一种学习方法，该方法并行使用两个动态加权图：一个用于对象像素，另一个用于背景像素。这两个图被集成到目标神经网络的损失函数中，通过现有标签优化模型，并最大程度地减少丢失标签的损坏。我们验证了我们对漫画图像中语音气球提取问题的处理方法。我们的方法使用气球提取算法的输出作为不完整的标签。结果与使用手动标签的最新监督方法相当。结果非常有希望，因为我们的方法不需要任何手动标签。此外，我们将我们的方法应用于医学图像分割任务以确认我们方法的一般性。结果非常有希望，因为我们的方法不需要任何手动标签。此外，我们将我们的方法应用于医学图像分割任务以确认我们方法的一般性。结果非常有希望，因为我们的方法不需要任何手动标签。此外，我们将我们的方法应用于医学图像分割任务以确认我们方法的一般性。