# Weakly supervised object detection using pseudo-strong labels

Source: [Weakly supervised object detection using pseudo-strong labels](<https://www.sota2.com/research/paper/68fa629c1e209131ce9fa27f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:22:26.903Z

Authors: Ke Yang, Dongsheng Li, Yong Dou, Shaohe Lv, Qiang Wang

Published: 2016-07-16T11:49:18.000Z

## Abstract

Object detection is an import task of computer vision.A variety of methods have been proposed,but methods using the weak labels still do not have a satisfactory result.In this paper,we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised model.One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset.A de-noise method is then applied to the noisy bounding boxes.Then the de-noised pseudo-strong labels are used to train a strongly object detection network.The whole framework is still weakly supervised because the entire process only uses the image-level labels.The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43.4% on mean average precision compared to 39.5% of the previous best result and 34.5% of the initial method,respectively.And this frame work is simple and distinct,and is promising to be applied to other method easily.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1607.04731>)

[PDF](<https://arxiv.org/pdf/1607.04731.pdf>)