open world detection问题定义 ORE: Open World Object Detector ORE的几个步骤 第一步:打框 第二步:对比聚类 related work 开放世界对象检测 open world object recognition,领域研究的目标主要是: (1)人具有辨别环境中未知物体的本能,希望模型也可以有鉴别unknown的能力; (2)人能够不断接收新事物,同时也不会遗忘...
Open-world object detection (OWOD) differs from traditional object detection by being more suited to real-world, dynamic scenarios. It aims to recognize unseen objects and have the skill to learn incrementally based on newly introduced knowledge. However, the current OWOD usually relies on ...
Zero-shot Object Detection/ Open-vocabulary Object Detection. 在该领域的早期设置中,zero-shot目标检测旨在将检测器从已知类别(训练)推广到未知类别(推理)。在这种设置下,各种作品[5]试图通过预训练的语义/文本特征[46、37、40、4、13]知识图[43、19、51、49]等来寻找已有类别和未知类别之间的关系。然而,这种...
impose a hierarchy betweenvisual and caption embeddings. We call our detector “Hy-perLearner”. We conduct extensive experiments on a widevariety of open-world detection benchmarks (COCO, LVIS,Object Detection in the Wild, RefCOCO) and our resultsshowthatourmodelconsistentlyoutperformsexistingstate-...
OW-DETR: Open-world Detection Transformer Supplementary Material Akshita Gupta* 1 Sanath Narayan* 1 K J Joseph2,4 Salman Khan4,3 Fahad Shahbaz Khan4,5 Mubarak Shah6 1Inception Institute of Artificial Intelligence 2IIT Hyderabad 3Australian National University 4Mohamed Bin Zayed University of...
Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Dis...
所以作者提出了“开放世界目标检测”任务。作者原文中对这个任务的解释如下: 1)在没有明确监督的情况下,将尚未引入该对象的对象识别为“未知”。 2)在逐步接收到相应的标签时,逐步学习这些已识别的未知类别,而不会忘记先前学习的课程。 对该任务的个人理解:...
each modality and performing information fusion with a cross-attention module to obtain the joint representation. For open-world detection, we use a multitask classifier that encompasses both a closed-world and an open-world classifier. The closed-world classifier is trained on the original data ...
Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. ...
named semi-supervised open-world detection (SS-OWOD), that reduces the annotation cost by casting the incremental learning stages of OWOD in a semi-supervised manner. We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting...