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.

上下文感知语义分割分类器的学习

Learning Context-aware Classifier for Semantic Segmentation

The wave of pre-training language models has been continuously improving the
quality of the machine-generated conversations, however, some of the generated
responses still suffer from excessive repetition, sometimes repeating words
from utterance, sometimes repeating words within self-generated responses, or
both. Inappropriate repetition of words can significantly degrade the quality
of the generated texts. Penalized sampling is one popular solution, reducing
the sampling probability of existing words during inference, however, it is
highly vulnerable to the inappropriate setting of the static weight. Setting it
too high can yield strange and unrealistic sentences while setting it too low
makes the task of suppressing repetition trivial. To remedy the shortcomings of
the above methods, we design a context-aware classifier to explicitly decide
when to allow repetition and when to employ penalized sampling. Such a
classifier can be easily integrated with existing decoding methods, reducing
repetitions where appropriate while preserving the diversity of the text.
Experimental results demonstrate that our method can generate higher quality
and more authentic dialogues.

本文研究基于上下文感知分类器的对话生成方法，通过对重复性的控制，提高了人工生成文本的质量和真实性。