Although supervised deep stereo matching networks have made impressive
achievements, the poor generalization ability caused by the domain gap prevents
them from being applied to real-life scenarios. In this paper, we propose to
leverage the feature of a model trained on large-scale datasets to deal with
the domain shift since it has seen various styles of images. With the cosine
similarity based cost volume as a bridge, the feature will be grafted to an
ordinary cost aggregation module. Despite the broad-spectrum representation,
such a low-level feature contains much general information which is not aimed
at stereo matching. To recover more task-specific information, the grafted
feature is further input into a shallow network to be transformed before
calculating the cost. Extensive experiments show that the model generalization
ability can be improved significantly with this broad-spectrum and
task-oriented feature. Specifically, based on two well-known architectures
PSMNet and GANet, our methods are superior to other robust algorithms when
transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is
available at this https URL

利用大规模数据集训练的模型特征，采用余弦相似度为基础的代价体积作为桥梁，将特征嫁接到一个普通的代价聚合模块，以此来解决领域转化带来的一般化能力不足。通过将嫁接特征输入到一个浅层网络进行变换计算代价，从而恢复更多的任务 - specific 信息，大量实验表明，这种宽光谱和任务定向的特征可以显著提高模型的泛化能力。

GraftNet: 基于广谱和任务取向特征的领域通用立体匹配

GraftNet: Towards Domain Generalized Stereo Matching with a  Broad-Spectrum and Task-Oriented Feature

Amid the pandemic COVID-19, the world is facing unprecedented infodemic with
the proliferation of both fake and real information. Considering the
problematic consequences that the COVID-19 fake-news have brought, the
scientific community has put effort to tackle it. To contribute to this fight
against the infodemic, we aim to achieve a robust model for the COVID-19
fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking
two separate approaches: 1) fine-tuning transformers based language models with
robust loss functions and 2) removing harmful training instances through
influence calculation. We further evaluate the robustness of our models by
evaluating on different COVID-19 misinformation test set (Tweets-19) to
understand model generalization ability. With the first approach, we achieve
98.13% for weighted F1 score (W-F1) for the shared task, whereas 38.18% W-F1 on
the Tweets-19 highest. On the contrary, by performing influence data cleansing,
our model with 99% cleansing percentage can achieve 54.33% W-F1 score on
Tweets-19 with a trade-off. By evaluating our models on two COVID-19 fake-news
test sets, we suggest the importance of model generalization ability in this
task to step forward to tackle the COVID-19 fake-news problem in online social
media platforms.

为了抗击 COVID-19 疫情期间社交媒体上的虚假信息，本文提出两种方法，一是 fine-tuning transformers-based language models，二是通过 influence data cleansing 去除危险的训练实例。通过在两个 COVID-19 虚假新闻测试集上评估模型，我们强调了此任务的模型泛化能力的重要性。最终模型可以达到高性能和模型泛化性能的平衡。