开放集任务 开放集任务(Open-set task)是指在机器学习和模式识别中,处理未知类别的识别或检测问题。传统的机器学习任务通常是封闭集任务(Closed-set task),即只考虑已知的类别,并且系统能够识别或检测到这些已知类别。而开放集任务则不仅考虑已知类别,还要处理那些未在训练数据中出现的未知类别。 在开放集任务中,模型...
为了符合 open set detection,HADG 将每个 \mathcal D_i 划分为两个互斥的集合 \mathcal D_i = \{\mathcal D_i^s, \mathcal D_i^q\},分别称为 support set 和 query set,分别用于训练和验证。并且保证两个集合中的正常样本属于两个完全不同的簇,两个集合的异常样本互不相同。 CDL CDL 用于对于 ...
Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely from normal videos are applicable to any testing anomalies but suffer from a high false positive rate. In ...
Official PyTorch implementation of the paper “Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection”, open-set anomaly detection, few-shot anomaly detection. - Choubo/DRA
Folders and files Name Last commit message Last commit date Latest commit History 93 Commits anodet notebooks tests .gitignore LICENSE README.md requirements.txt setup.cfg setup.py README MIT license anodet A set of functions and classes for performing anomaly detection in images using features ...
Current video anomaly detection (VAD) approaches with weak supervisions are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic ...
因此,作者提出了一种更符合实际的研究问题——开集半监督目标检测(Open-set Semi-supervised Object Detection,OSSOD)。如图1(a),标注数据与一般的SSOD保持一致,只有分布内(In-distribution, OOD) 的类别。而无标注的数据里ID和OOD的类别的物体同时存在。由于OOD数据的干扰,经过实验发现,一般的SSOD算法性能出现下降...
Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector ... BKS Isaac-Medina,YFA Gaus,N Bhowmik,... - European ...
Open-set Recognition (OSR) and Out-of-distribution Detection (OoDD)。OSR（开集识别）和OoDD（外部...
Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categori