Despite increasing efforts on universal representations for visual
recognition, few have addressed object detection. In this paper, we develop an
effective and efficient universal object detection system that is capable of
working on various image domains, from human faces and traffic signs to medical
CT images. Unlike multi-domain models, this universal model does not require
prior knowledge of the domain of interest. This is achieved by the introduction
of a new family of adaptation layers, based on the principles of squeeze and
excitation, and a new domain-attention mechanism. In the proposed universal
detector, all parameters and computations are shared across domains, and a
single network processes all domains all the time. Experiments, on a newly
established universal object detection benchmark of 11 diverse datasets, show
that the proposed detector outperforms a bank of individual detectors, a
multi-domain detector, and a baseline universal detector, with a 1.3x parameter
increase over a single-domain baseline detector. The code and benchmark will be
released at this http URL

本研究提出了一种基于通用表示法的物体检测系统，通过引入适应层和域注意力机制，实现了跨域检测，且在 11 个数据集上表现优于其他检测器。

基于域注意力的通用物体检测

Towards Universal Object Detection by Domain Attention

It is today acknowledged that neural network language models outperform
backoff language models in applications like speech recognition or statistical
machine translation. However, training these models on large amounts of data
can take several days. We present efficient techniques to adapt a neural
network language model to new data. Instead of training a completely new model
or relying on mixture approaches, we propose two new methods: continued
training on resampled data or insertion of adaptation layers. We present
experimental results in an CAT environment where the post-edits of professional
translators are used to improve an SMT system. Both methods are very fast and
achieve significant improvements without overfitting the small adaptation data.

本文提出了两种优化神经网络语言模型适应新数据的方法，包括在重新采样数据上进行继续训练或插入适应性层。在 CAT 环境中应用于 SMT 系统中，这两种方法均取得了显著的改进。