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。
在每个解码器块中,object queries首先在它们之间执行self-attention,然后对feature token进行cross-attention。为简单起见,图2只给出了交叉注意部分。请参阅附录以获得DETR解码器的完整视图。经过多个self-attention和cross-attentio块后,object queries的output states生成实例级检测预测(instance-level detection predictions)...
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 (WSOD) is the task of training object detectors with only image tag supervisions. <span style="color:grey; opacity: 0.6">( Image credit: [Soft Proposal Networks for Weakly Supervised Object Localization](https://arxiv.
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....
WSDDN通过融合CNN的特征提取与传统MIL方法，显著改进了特征表示，提升了检测性能。OICR则针对WSDDN中局部聚焦问题，提出在线实例分类器修正，通过多次instance refinement来修正proposals的得分，避免了局部最优解。Towards Precise End-to-end Weakly Supervised Object Detection Network则尝试了端到端的联合训练，...
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection Krishna Kumar Singh, Fanyi Xiao, and Yong Jae Lee University of California, Davis Abstract The status quo approach to training object detectors re- quires expensive bounding box annotations....
[paper reading] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection CVPR2019 MIL陷入局部最优,检测到局部,无法完整的检测到物体。将instance划分为空间相关和类别相关的子集。在这些子集中定义一系列平滑的损失近似代替原损失函数,优化这些平滑损失。