论文标题
通用几个射击语义分段的强大基线
A Strong Baseline for Generalized Few-Shot Semantic Segmentation
论文作者
论文摘要
本文介绍了具有直接的训练过程和易于优化的推理阶段的广义少量分割框架。特别是，我们提出了一个基于众所周知的信息原理的简单而有效的模型，其中最大化了学识渊博的特征表示及其相应预测之间的相互信息（MI）。此外，从我们的基于MI的配方中得出的术语与知识蒸馏术语相结合，以保留基本类别的知识。通过简单的培训过程，我们的推论模型可以应用于在基础类中训练的任何细分网络的基础上。所提出的推论可以对流行的几季分段基准，Pascal-5^i $和Coco- $ 20^i $产生实质性改进。特别是，对于新颖的课程，在1-shot和5-Shot方案中，改善的增长分别从7％到26％（Pascal-5^i $），从3％到12％（可可$ 20^i $）。此外，我们提出了一个更具挑战性的环境，在这种情况下，性能差距进一步加剧了。我们的代码可在https://github.com/sinahmr/diam上公开获取。
This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions is maximized. In addition, the terms derived from our MI-based formulation are coupled with a knowledge distillation term to retain the knowledge on base classes. With a simple training process, our inference model can be applied on top of any segmentation network trained on base classes. The proposed inference yields substantial improvements on the popular few-shot segmentation benchmarks, PASCAL-$5^i$ and COCO-$20^i$. Particularly, for novel classes, the improvement gains range from 7% to 26% (PASCAL-$5^i$) and from 3% to 12% (COCO-$20^i$) in the 1-shot and 5-shot scenarios, respectively. Furthermore, we propose a more challenging setting, where performance gaps are further exacerbated. Our code is publicly available at https://github.com/sinahmr/DIaM.
