How do we build a general and broad object detection system? We use all
labels of all concepts ever annotated. These labels span diverse datasets with
potentially inconsistent taxonomies. In this paper, we present a simple method
for training a unified detector on multiple large-scale datasets. We use
dataset-specific training protocols and losses, but share a common detection
architecture with dataset-specific outputs. We show how to automatically
integrate these dataset-specific outputs into a common semantic taxonomy. In
contrast to prior work, our approach does not require manual taxonomy
reconciliation. Experiments show our learned taxonomy outperforms a
expert-designed taxonomy in all datasets. Our multi-dataset detector performs
as well as dataset-specific models on each training domain, and can generalize
to new unseen dataset without fine-tuning on them. Code is available at
this https URL

本文提出一种简单的方法，使用所有数据集上的标签训练一个统一的多数据集物体检测器，它可以自动整合数据特定输出到一个通用的语义分类体系中，避免了手动分类调和的步骤，且实验表明，该流程可以推广到新的未见数据集，而无需再次针对它们进行微调。

简易多数据集检测

Simple multi-dataset detection

Modern CNN-based object detectors assign anchors for ground-truth objects
under the restriction of object-anchor Intersection-over-Unit (IoU). In this
study, we propose a learning-to-match approach to break IoU restriction,
allowing objects to match anchors in a flexible manner. Our approach, referred
to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor
matching by formulating detector training as a maximum likelihood estimation
(MLE) procedure. FreeAnchor targets at learning features which best explain a
class of objects in terms of both classification and localization. FreeAnchor
is implemented by optimizing detection customized likelihood and can be fused
with CNN-based detectors in a plug-and-play manner. Experiments on COCO
demonstrate that FreeAnchor consistently outperforms their counterparts with
significant margins.

本文提出了一种名为 FreeAnchor 的学习对应方法来打破目标 - 锚点 IoU 的限制，更新手工制定的锚点分配为自由的锚点匹配，并通过最大似然估计（MLE）过程来实现检测器训练。该方法通过优化检测有效似然来实现，可以与基于 CNN 的检测器插入并组合使用，实验结果表明，FreeAnchor 在 COCO 数据集上表现优异。

FreeAnchor：学习匹配锚点以进行视觉目标检测

FreeAnchor: Learning to Match Anchors for Visual Object Detection

Important gains have recently been obtained in object detection by using
training objectives that focus on {\em hard negative} examples, i.e., negative
examples that are currently rated as positive or ambiguous by the detector.
These examples can strongly influence parameters when the network is trained to
correct them. Unfortunately, they are often sparse in the training data, and
are expensive to obtain. In this work, we show how large numbers of hard
negatives can be obtained {\em automatically} by analyzing the output of a
trained detector on video sequences. In particular, detections that are {\em
isolated in time}, i.e., that have no associated preceding or following
detections, are likely to be hard negatives. We describe simple procedures for
mining large numbers of such hard negatives (and also hard {\em positives})
from unlabeled video data. Our experiments show that retraining detectors on
these automatically obtained examples often significantly improves performance.
We present experiments on multiple architectures and multiple data sets,
including face detection, pedestrian detection and other object categories.

通过对视频数据分析，提出一种利用孤立检测结果自动获取大量 hard negative 和 hard positive 的方法，用于在多项物体检测数据集中重新训练神经网络并显著提升检测表现。