For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective. This regression objective optimizes the number of occurrences of the target object in ...
弱监督论文阅读《object instance mining for weakly supervised object detection》,程序员大本营,技术文章内容聚合第一站。
Weakly Supervised Object Detection with Segmentation Collaboration [5] Li X, Kan M, Shan S, et al. Weakly supervised object detection with segmentation collaboration[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 9735-9744. 本文中将弱监督分割和检测网络联合在一起...
【论文阅读】【弱监督-3D目标检测】-Weakly Supervised 3D Object Detection from Lidar Point Cloud,程序员大本营,技术文章内容聚合第一站。
Weakly Supervised Object Detection的简要介绍如下：定义：WSOD是利用图像级标签进行目标分类与定位的技术。主要挑战： Discriminative Region Problem：网络在训练时可能只关注物体的核心或易于识别的部分，导致定位不准确。 Multiple Instance Problem：图像中可能存在多个同类别实例，而WSOD训练方式难以准确识别所有...
WSDDN全称是Weakly Supervised Deep Detection Network,即弱监督深度检测网络。 只依靠image级别的label来对其训练 整个结构主要是以图像分类和目标检测这两个框架 左边是一个预训练好的CNN 右边则是一个类似FasterRCNN结构,通过感兴趣池化以及SPP空间金字塔池化,得到区域(region)级别的特征 然后分支成两个数据流,其中一...
Fully-supervised object detection (FSOD) and weakly-supervised object detection (WSOD) are two extremes in the field of object detection. The former relies entirely on detailed bounding-box annotations while the later discards them completely. To balance these two extremes, we propose to make use ...
The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its practicality. In this work we propose Random Prompt Assisted Weakly-supervised 3D Object Detection termed as Prompt3D a weakly-supervised approach that leverages position-level labels to overcome this challenge...
Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions. ( Image credit: Soft Proposal Networks for Weakly Supervised Object Localization )Benchmarks Add a Result These leaderboards are used to track progress in Weakly Supervised Object ...
Weakly Supervised Object Detection（WSOD）利用图像级标签进行目标分类与定位，主要面临挑战包括：1. **Discriminative Region Problem**：网络在训练过程中产生多个区域提议，其中包含物体核心区域的提议得分最高。若仅依据提议得分选择边界框，可能会导致定位不准确，因为网络可能只关注于物体中易于识别的部分...