To tackle the semantic segmentation task, which is a fundamental problem in computer vision, various approaches have been proposed. However, how to utilize object interaction information for improving semantic segmentation performances is not paid enough attention to. In this paper, we propose a method for information aggregation and fusion for exploring object interaction information effectively for improving semantic segmentation performances. Specifically, we propose a logit aggregation strategy to explore object interaction information for semantic segmentation. Furthermore, to facilitate object interaction to guide the training of the semantic segmentation model, we propose to fuse features from intermediate layers of the model to aid pixel semantic label predication. And to fuse these features effectively, a buffered layer connection approach is presented. The proposed method is evaluated extensively in experiments. Obtained results demonstrate the effectiveness of the proposed method.

为了解决语义分割任务，这是计算机视觉中的基本问题，已经提出了各种方法。然而，如何利用对象交互信息来提高语义分割性能还没有引起足够的重视。本文提出了一种信息聚合与融合的方法，可以有效地探索对象交互信息，从而提高语义分割的性能。具体来说，我们提出了一种Logit聚合策略，以探索对象交互信息以进行语义分割。此外，为了促进对象交互以指导语义分割模型的训练，我们建议融合模型中间层的特征以辅助像素语义标签预测。为了有效融合这些功能，提出了一种缓冲层连接方法。该方法在实验中得到了广泛的评价。所得结果证明了该方法的有效性。