Most image-to-image translation methods require a large number of training
images, which restricts their applicability. We instead propose ManiFest: a
framework for few-shot image translation that learns a context-aware
representation of a target domain from a few images only. To enforce feature
consistency, our framework learns a style manifold between source and proxy
anchor domains (assumed to be composed of large numbers of images). The learned
manifold is interpolated and deformed towards the few-shot target domain via
patch-based adversarial and feature statistics alignment losses. All of these
components are trained simultaneously during a single end-to-end loop. In
addition to the general few-shot translation task, our approach can
alternatively be conditioned on a single exemplar image to reproduce its
specific style. Extensive experiments demonstrate the efficacy of ManiFest on
multiple tasks, outperforming the state-of-the-art on all metrics and in both
the general- and exemplar-based scenarios. Our code is available at
this https URL .

ManiFest 是一个针对 Few-shot 图像翻译的框架，利用少量图像学习目标域的上下文感知表示，并学习源域和代理锚定域之间的样式流形以强制实现特征一致性，该学习的流形通过基于图像块的对抗和特征统计对齐损失插值并向 Few-shot 目标领域畸变。该方法在多项任务中证明了其有效性，优于现有技术，并提供了代码。

ManiFest: 少样本图像转换的流形变形

ManiFest: Manifold Deformation for Few-shot Image Translation

Solving grounded language tasks often requires reasoning about relationships
between objects in the context of a given task. For example, to answer the
question "What color is the mug on the plate?" we must check the color of the
specific mug that satisfies the "on" relationship with respect to the plate.
Recent work has proposed various methods capable of complex relational
reasoning. However, most of their power is in the inference structure, while
the scene is represented with simple local appearance features. In this paper,
we take an alternate approach and build contextualized representations for
objects in a visual scene to support relational reasoning. We propose a general
framework of Language-Conditioned Graph Networks (LCGN), where each node
represents an object, and is described by a context-aware representation from
related objects through iterative message passing conditioned on the textual
input. E.g., conditioning on the "on" relationship to the plate, the object
"mug" gathers messages from the object "plate" to update its representation to
"mug on the plate", which can be easily consumed by a simple classifier for
answer prediction. We experimentally show that our LCGN approach effectively
supports relational reasoning and improves performance across several tasks and
datasets. Our code is available at this http URL

本文提出了一种基于场景上下文化表征的图网络模型，通过文本输入的迭代信息传递来构建物体的语境化表达，以支持关系推理，实验结果表明该方法有效地提高了多项任务和数据集的表现。