Towards Class-incremental Object Detection with Nearest Mean of Exemplars
Incremental learning is a form of online learning. Incremental learning can modify the parameters and structure of the deep learning model so that the model does not forget the old knowledge while learning new knowledge. Preventing catastrophic forgetting is the most important task of incremental learning. However, the current incremental learning is often only for one type of input. For example, if the input images are of the same type, the current incremental model can learn new knowledge while not forgetting old knowledge. However, if several categories are added to the input graphics, the current model will not be able to deal with it correctly, and the accuracy will drop significantly. Therefore, this paper proposes a kind of incremental method, which adjusts the parameters of the model by identifying the prototype vector and increasing the distance of the vector, so that the model can learn new knowledge without catastrophic forgetting. Experiments show the effectiveness of our proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
Class-Incremental Object DetectionIncremental Learningobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Deep Nearest Class Mean Model for Incremental Odor Classification
In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training datasets are static. However, for some odor recognition ta…
ClassificationGeneral ClassificationIncrementally Zero-Shot Detection by an Extreme Value Analyzer
Human beings not only have the ability to recognize novel unseen classes, but also can incrementally incorporate the new classes to existing knowledge preserved. However, zero-shot learning models assume that all seen cl…
class-incremental learningClass Incremental LearningIncremental Learningobject-detection+2I3DOD: Towards Incremental 3D Object Detection via Prompting
3D object detection has achieved significant performance in many fields, e.g., robotics system, autonomous driving, and augmented reality. However, most existing methods could cause catastrophic forgetting of old classes…
3D Object DetectionAutonomous DrivingObjectobject-detection+2Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on increme…
Few-Shot Object DetectionKnowledge DistillationObjectobject-detection+2DualMem: Bypassing the Objectness Bottleneck for Calibrated Unknown-Stream Filtering in Open-World Object Detection
Open-world object detection (OWOD) requires detectors to localize known classes while identifying unknown objects for future incremental learning. We find that the unknown prediction streams of strong OWOD detectors are …
Incremental LearningObject Detection