Object-based Novelty Detection (ND) aims to identify unknown objects that do not belong to classes seen during training by an object detection model. The task is particularly crucial in real-world applications, as it allows to avoid potentially harmful behaviours, e.g. as in the case of object detection models adopted in a self-driving car or in an autonomous robot. Traditional approaches to ND focus on one time offline post processing of the pretrained object detection output, leaving no possibility to improve the model robustness after training and discarding the abundant amount of out-of-distribution data encountered during deployment. In this work, we propose a novel framework for object-based ND, assuming that human feedback can be requested on the predicted output and later incorporated to refine the ND model without negatively affecting the main object detection performance. This refinement operation is repeated whenever new feedback is available. To tackle this new formulation of the problem for object detection, we propose a lightweight ND module attached on top of a pre-trained object detection model, which is incrementally updated through a feedback loop. We also propose a new benchmark to evaluate methods on this new setting and test extensively our ND approach against baselines, showing increased robustness and a successful incorporation of the received feedback.

翻译：物体新奇性检测旨在识别不属于目标检测模型训练时所见过类别的未知物体。该任务在现实应用中尤为关键，因为其可避免潜在危险行为，例如自动驾驶汽车或自主机器人中采用的目标检测模型可能引发的状况。传统新奇性检测方法侧重于对预训练目标检测输出进行一次性离线后处理，既无法在训练后提升模型鲁棒性，也浪费了部署过程中遇到的大量分布外数据。本研究提出一种新颖的物体新奇性检测框架，假设可针对预测输出请求人工反馈，并在不影响主要目标检测性能的前提下将反馈融入新奇性检测模型的优化过程。每当获得新反馈时，将重复执行该优化操作。为应对目标检测中这一新问题形式，我们提出在预训练目标检测模型上附加轻量级新奇性检测模块，并通过反馈循环进行增量式更新。我们还建立了评估该新场景下各方法的新基准，并基于基线方法对提出的新奇性检测方法进行广泛测试，结果表明该方法显著提升了鲁棒性，成功实现了所获反馈的融合。