A common practice in deep learning consists of training large neural networks
on massive datasets to perform accurately for different domains and tasks.
While this methodology may work well in numerous application areas, it only
applies across modalities due to a larger distribution shift in data captured
using different sensors. This paper focuses on the problem of adapting a large
object detection model to one or multiple modalities while being efficient. To
do so, we propose ModTr as an alternative to the common approach of fine-tuning
large models. ModTr consists of adapting the input with a small transformation
network trained to minimize the detection loss directly. The original model can
therefore work on the translated inputs without any further change or
fine-tuning to its parameters. Experimental results on translating from IR to
RGB images on two well-known datasets show that this simple ModTr approach
provides detectors that can perform comparably or better than the standard
fine-tuning without forgetting the original knowledge. This opens the doors to
a more flexible and efficient service-based detection pipeline in which,
instead of using a different detector for each modality, a unique and unaltered
server is constantly running, where multiple modalities with the corresponding
translations can query it. Code: this https URL

通过训练小型的转换网络直接减小检测损失，该论文提出了一种新的方法 ModTr，用于将大型目标检测模型适应于一个或多个不同模态数据，从而实现更加灵活和高效的服务式检测流水线。

无忘记先验知识的目标检测适应模态转换

Modality Translation for Object Detection Adaptation Without Forgetting  Prior Knowledge

Fashion manipulation has attracted growing interest due to its great
application value, which inspires many researches towards fashion images.
However, little attention has been paid to fashion design draft. In this paper,
we study a new unaligned translation problem between design drafts and real
fashion items, whose main challenge lies in the huge misalignment between the
two modalities. We first collect paired design drafts and real fashion item
images without pixel-wise alignment. To solve the misalignment problem, our
main idea is to train a sampling network to adaptively adjust the input to an
intermediate state with structure alignment to the output. Moreover, built upon
the sampling network, we present design draft to real fashion item translation
network (D2RNet), where two separate translation streams that focus on texture
and shape, respectively, are combined tactfully to get both benefits. D2RNet is
able to generate realistic garments with both texture and shape consistency to
their design drafts. We show that this idea can be effectively applied to the
reverse translation problem and present R2DNet accordingly. Extensive
experiments on unaligned fashion design translation demonstrate the superiority
of our method over state-of-the-art methods. Our project website is available
at: this https URL .

本文利用采样网络建立设计草稿与真实时装的翻译模型，实现设计草稿向真实时装的转化以及真实时装向设计草稿的转化，并通过实验证明了该模型的卓越性能。