End-to-end weakly supervised semantic segmentation aims at optimizing a
segmentation model in a single-stage training process based on only image
annotations. Existing methods adopt an online-trained classification branch to
provide pseudo annotations for supervising the segmentation branch. However,
this strategy makes the classification branch dominate the whole concurrent
training process, hindering these two branches from assisting each other. In
our work, we treat these two branches equally by viewing them as diverse ways
to generate the segmentation map, and add interactions on both their
supervision and operation to achieve mutual promotion. For this purpose, a
bidirectional supervision mechanism is elaborated to force the consistency
between the outputs of these two branches. Thus, the segmentation branch can
also give feedback to the classification branch to enhance the quality of
localization seeds. Moreover, our method also designs interaction operations
between these two branches to exchange their knowledge to assist each other.
Experiments indicate our work outperforms existing end-to-end weakly supervised
segmentation methods.

我们的研究通过采用双向监督机制，将分类和分割两个支路视为多样性生成分割图的方法，并增加其相互作用，以实现相互促进。我们的方法在两个支路的输出上强制一致性，使得分割支路也能为分类支路提供反馈并提高定位种子的质量。此外，我们的方法还设计了两个支路之间的交互操作，以交换知识并相互协助。实验证明我们的方法优于现有的端到端弱监督分割方法。

分支互相促进的端到端弱监督语义分割

Branches Mutual Promotion for End-to-End Weakly Supervised Semantic  Segmentation

Learning the generalizable feature representation is critical for few-shot
image classification. While recent works exploited task-specific feature
embedding using meta-tasks for few-shot learning, they are limited in many
challenging tasks as being distracted by the excursive features such as the
background, domain and style of the image samples. In this work, we propose a
novel Disentangled Feature Representation framework, dubbed DFR, for few-shot
learning applications. DFR can adaptively decouple the discriminative features
that are modeled by the classification branch, from the class-irrelevant
component of the variation branch. In general, most of the popular deep
few-shot learning methods can be plugged in as the classification branch, thus
DFR can boost their performance on various few-shot tasks. Furthermore, we
propose a novel FS-DomainNet dataset based on DomainNet, for benchmarking the
few-shot domain generalization tasks. We conducted extensive experiments to
evaluate the proposed DFR on general and fine-grained few-shot classification,
as well as few-shot domain generalization, using the corresponding four
benchmarks, i.e., mini-ImageNet, tiered-ImageNet, CUB, as well as the proposed
FS-DomainNet. Thanks to the effective feature disentangling, the DFR-based
few-shot classifiers achieved the state-of-the-art results on all datasets.

本篇论文提出了一种名为 DFR 的新型解缩特征表示框架，它可以适应性地解耦由分类分支建模的有区别性特征和变差分支的类不相关的成分，从而提升了各种少样本任务上深度学习方法的性能，这是通过将其分类分支嵌入 DFR 实现的并在 mini-ImageNet、tiered-ImageNet、CUB 和 FS-DomainNet 等四个数据集上进行了相关实验和测试得出的。