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...
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multiple instance learning (MIL) and have non-convex loss functions which are prone to get...
简读:Object-Aware Instance Labeling 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 ：结合弱监督分割与检测网络，提出协作网络结构，通过生成对抗定位策略优化分割结果与分类任务。WSOD在图像级标签下实现目标检测，有效应对弱监督学习中的挑战，推动了目标检测技术的发展。
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 本文首发于GiantPandaCV公众号 摘要 近些年来,因为弱监督目标检测仅需要图片分类级别的label受到了人们广泛的关注,其代价是准确率一定程度的下降。本文提出了一个简单而有效的弱监督协作目标检测框架,基于共享部分特征,增强预测相关性来同时训练强,弱监督两个检测网络。弱监督目标检测...
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 ...
Recent progress in weakly supervised object detection is featured by a combination of multiple instance detection networks (MIDN) and ordinal online refinement. However, with only image-level annotation, MIDN inevitably assigns high scores to some unexpected region proposals when generating pseudo labels....
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...
Weakly supervised object detection (WSOD) aims to classify and locate objects with only image-level supervision. Many WSOD approaches adopt multiple instance learning as the initial model, which is prone to converge to the most discriminative object regions while ignoring the whole object, and therefo...