Therefore, we propose a novel Class-Incremental Object Detection (CIOD) framework based on deep learning. CIOD divides object detection into two stages: object candidate box generation and selection. In the first stage, we improved the traditional OpenCV cascaded classifier to adapt to class-...
With the rapid development of remote sensing technology and the growing demand for applications, the classical deep learning-based object detection model is bottlenecked in processing incremental data, especially in the increasing classes of detected objects. It requires models to sequentially learn new ...
The incrementalOneClassSVM function creates an incrementalOneClassSVM model object, which represents a one-class SVM model for incremental anomaly detection.
Really, does this support the class-incremental object detection? Can be the training time saved? if so, does not the knowledge forgetting occur? The following papers treated such problems in the incremental object detection. https://arxiv.org/abs/1708.06977 https://arxiv.org/abs/2003.04668 htt...
Incremental Learning: Most studies in incremental learn- ing have focused on object detection and classification problems [5, 30, 41, 43, 47]. Some of these works use replay-based approaches, which store samples from pre- vious ta...
目标检测中的应用 (Applications in object detection) 目标检测是计算机视觉中的一个重要任务,FSCIL技术在此领域的应用允许计算机系统通过少量样本学习检测新对象。 例如,Kang等人 (2019) 提出了一种新颖的少样本检测模型,但由于模型缺乏从数据流中增量学习新目标的能力,限制了其在开放环境和边缘设备中的实际部署。
incrementalOneClassSVM model object Incremental one-class SVM model, specified as an incrementalOneClassSVM model object. You can create Mdl directly or by converting a supported, traditionally trained machine learning model using the incrementalLearner function. For more details, see the incrementalOne...
In the field of class incremental learning (CIL), generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the continuous improvements in generative models. However, its application in class incremental object detection (CIOD) has been significantly...
situation can be found, e.g., in spam filtering, biometrics, medical decision support, or fraud detection to enumerate only a few. The concept drift in the classification model mean that the statistical dependencies between attributes describing an object and its predefined label could change over...
Knowledge transferDespite the remarkable performance achieved by DNN-based object detectors, class incremental object detection (CIOD) remains a challenge, in which the network has to learn to detect novel classes sequentially. Catastrophic forgetting is the main problem underlying this difficulty, as ...