Semantic segmentation is still a challenging task for parsing diverse
contexts in different scenes, thus the fixed classifier might not be able to
well address varying feature distributions during testing. Different from the
mainstream literature where the efficacy of strong backbones and effective
decoder heads has been well studied, in this paper, additional contextual hints
are instead exploited via learning a context-aware classifier whose content is
data-conditioned, decently adapting to different latent distributions. Since
only the classifier is dynamically altered, our method is model-agnostic and
can be easily applied to generic segmentation models. Notably, with only
negligible additional parameters and +2\% inference time, decent performance
gain has been achieved on both small and large models with challenging
benchmarks, manifesting substantial practical merits brought by our simple yet
effective method. The implementation is available at
https://github.com/tianzhuotao/CAC.

利用数据依赖的上下文感知分类器通过学习附加环境提示，适应不同的潜在分布，从而解决分割不同场景多样化特征分布的困难，进而显著提高分割模型的性能.