Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization

2019-10-04 · Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang

In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations. The proposed approach is built on the observation that the proposal set from the training dataset is a collection of background, object parts, and objects. Several strategies are taken to adaptively eliminate the noisy proposals and generate pseudo object-level annotations for the weakly labeled dataset. A multiple instance learning (MIL) algorithm enhanced by mask-out strategy is adopted to collect the class-specific object proposals, which are then utilized to adapt a pre-trained classification network to a detection network. In addition, the detection results from the detection network are re-weighted by jointly considering the detection scores and the overlap ratio of proposals in a proposal subset optimization framework. The optimal proposals work as object-level labels that enable a pseudo-strongly supervised dataset for training the detection network. Consequently, we establish a fully adaptive detection network. Extensive evaluations on the PASCAL VOC 2007 and 2012 datasets demonstrate a significant improvement compared with the state-of-the-art methods.

📄 PDF Abstract BibTeX arXiv:1910.02101

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Denoising Multiple Instance LearningObjectObject Localization Weakly-Supervised Object Localization

Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization

2019-10-04 · arXiv 2019 10 · Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang

Denoising Multiple Instance Learning Object Object Localization+1

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2019-06-01 · CVPR 2019 6 · Krishna Kumar Singh, Yong Jae Lee

We propose a novel way of using videos to obtain high precision object proposals for weakly-supervised object detection. Existing weakly-supervised detection approaches use off-the-shelf proposal methods like edge boxes…

Object object-detection Object Detection Region Proposal+1

Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy

2016-10-05 · Dan Barnes, Will Maddern, Ingmar Posner

We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our propose…

Autonomous Driving Segmentation Semantic Segmentation Weakly supervised segmentation

Weakly Supervised Region Proposal Network and Object Detection

2018-09-01 · ECCV 2018 9 · Peng Tang, Xinggang Wang, Angtian Wang, Yongluan Yan 외

The Convolutional Neural Network (CNN) based region proposal generation method (i.e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors…

Object object-detection Object Detection Region Proposal+1

Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection

2024-01-01 · CVPR 2024 1 · Xiaohong Zhang, Huisheng Ye, Jingwen Li, Qinyu Tang 외

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 w…

3D Object Detection object-detection Object Detection Scene Generation