Object detectionActive learning (AL) for object detection (OD) aims to reduce labeling costs by selecting the most valuable samples that enhance the detection network from the unlabeled pool. Due to the complexity of OD compared with image classification, more consideration should be given when ...
Active Learning for Deep Object DetectionClemens-Alexander Brust 1 , Christoph Käding 1,2 and Joachim Denzler 1,21 Computer Vision Group, Friedrich Schiller University Jena, Germany2 Michael Stifel Center Jena, Germany{f author, s author}@uni-jena.deKeywords: Active Learning, Deep Learning, ...
Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable...
detection, making a shallow-to-deep transition. Therefore, deep learning plays a crucial role in defect detection at present. Many defect detection methods are proposed based on deep object detection. For example, Feng et al. [16] built an improved version based on the YOLO model, which is ...
这是Multiple Instance Active Learning for Object Detection(用于目标检测的多示例主动学习方法) , CVPR 2021 一文的代码。 任务描述 在本文中,我们提出了 多示例主动目标检测(MI-AOD) ,通过观察示例级的不确定性来选择信息量最大的图像用于检测器的训练。 主动目标检测(用于目标检测的主动学习)的流程如下图所示。
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data...
With the development of CNN, deep active learning for object detection has been extensively studied. The uncertainty-based methods are widely used for querying samples. Feng et al. [33] use MC dropout and Deep Ensembles to obtain uncertainty estimations and select the most uncertain samples by ...
Automatic adaptation of object detectors to new domains using self-training | [CVPR' 19] |[pdf] [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR' 19] |[pdf] [FSAF] Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR' 19] |[pdf] [...
Deep Learning for Object Detection: A Comprehensive Review Review of Deep Learning Algorithms for Object Detection A Simple Guide to the Versions of the Inception Network R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms A gentle guide to deep learning object detection The int...
解决办法是提出新的框架orz,比如给query strategy设计个损失预测模型(learning loss for AL)。 3 DEEP ACTIVE LEARNING 3.1 Query Strategy Optimization in DAL 优化query strategy的本质是在保证准确度的情况下降低标注成本。 3.1.1 Batch Mode DAL (BMDAL) Batch Mode DAL是一类Query strategy优化方法的总称,即从...