Despite the recent advances in the field of object detection, common
architectures are still ill-suited to incrementally detect new categories over
time. They are vulnerable to catastrophic forgetting: they forget what has been
already learned while updating their parameters in absence of the original
training data. Previous works extended standard classification methods in the
object detection task, mainly adopting the knowledge distillation framework.
However, we argue that object detection introduces an additional problem, which
has been overlooked. While objects belonging to new classes are learned thanks
to their annotations, if no supervision is provided for other objects that may
still be present in the input, the model learns to associate them to background
regions. We propose to handle these missing annotations by revisiting the
standard knowledge distillation framework. Our approach outperforms current
state-of-the-art methods in every setting of the Pascal-VOC dataset. We further
propose an extension to instance segmentation, outperforming the other
baselines. Code can be found here: this https URL

本研究研究对象检测中的遗忘问题，提出新的知识蒸馏框架处理缺失标签，并在 Pascal-VOC 数据集中取得了领先的效果，同时在实例分割模型中有所拓展。

目标检测中的增量学习中建模缺失注释

Modeling Missing Annotations for Incremental Learning in Object  Detection

Research shows a noticeable drop in performance of object detectors when the
training data has missing annotations, i.e. sparsely annotated data.
Contemporary methods focus on proxies for missing ground-truth annotations
either in the form of pseudo-labels or by re-weighing gradients for unlabeled
boxes during training. In this work, we revisit the formulation of sparsely
annotated object detection. We observe that sparsely annotated object detection
can be considered a semi-supervised object detection problem at a region level.
Building on this insight, we propose a region-based semi-supervised algorithm,
that automatically identifies regions containing unlabeled foreground objects.
Our algorithm then processes the labeled and un-labeled foreground regions
differently, a common practice in semi-supervised methods. To evaluate the
effectiveness of the proposed approach, we conduct exhaustive experiments on
five splits commonly used by sparsely annotated approaches on the PASCAL-VOC
and COCO datasets and achieve state-of-the-art performance. In addition to
this, we show that our approach achieves competitive performance on standard
semi-supervised setups demonstrating the strength and broad applicability of
our approach.

本文提出一种基于区域的半监督算法，用于解决稀疏标注数据中缺失标注信息的物体检测问题，实验表明该算法在 PASCAL-VOC 数据集中取得了最优性能。

基于区域的半监督稀疏标注目标检测方法

Sparsely Annotated Object Detection: A Region-based Semi-supervised  Approach

We study the robustness of object detection under the presence of missing
annotations. In this setting, the unlabeled object instances will be treated as
background, which will generate an incorrect training signal for the detector.
Interestingly, we observe that after dropping 30% of the annotations (and
labeling them as background), the performance of CNN-based object detectors
like Faster-RCNN only drops by 5% on the PASCAL VOC dataset. We provide a
detailed explanation for this result. To further bridge the performance gap, we
propose a simple yet effective solution, called Soft Sampling. Soft Sampling
re-weights the gradients of RoIs as a function of overlap with positive
instances. This ensures that the uncertain background regions are given a
smaller weight compared to the hardnegatives. Extensive experiments on curated
PASCAL VOC datasets demonstrate the effectiveness of the proposed Soft Sampling
method at different annotation drop rates. Finally, we show that on
OpenImagesV3, which is a real-world dataset with missing annotations, Soft
Sampling outperforms standard detection baselines by over 3%.

探究了在缺失标注情况下，目标检测的鲁棒性。实验显示通过 Soft Sampling 方法对梯度进行加权能够提高检测成功率，尤其在真实数据集中的表现优于标准检测基线。