# Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning

Source: [Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning](<https://www.sota2.com/research/paper/68fa63331e209131ce9fd5de>)

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

Data updated: 2025-12-14T16:40:52.153Z

Authors: Dingwen Zhang, Deyu Meng, Long Zhao, Junwei Han

Published: 2017-03-03T18:55:10.000Z

## Abstract

Weakly-supervised object detection (WOD) is a challenging problems in computer vision. The key problem is to simultaneously infer the exact object locations in the training images and train the object detectors, given only the training images with weak image-level labels. Intuitively, by simulating the selective attention mechanism of human visual system, saliency detection technique can select attractive objects in scenes and thus is a potential way to provide useful priors for WOD. However, the way to adopt saliency detection in WOD is not trivial since the detected saliency region might be possibly highly ambiguous in complex cases. To this end, this paper first comprehensively analyzes the challenges in applying saliency detection to WOD. Then, we make one of the earliest efforts to bridge saliency detection to WOD via the self-paced curriculum learning, which can guide the learning procedure to gradually achieve faithful knowledge of multi-class objects from easy to hard. The experimental results demonstrate that the proposed approach can successfully bridge saliency detection and WOD tasks and achieve the state-of-the-art object detection results under the weak supervision.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Localization on PASCAL VOC 2007 (trainval)](<https://www.sota2.com/research/sota/object-localization-on-pascal-voc-2007-trainval.md>): Object Localization; PASCAL VOC 2007 (trainval); CorLoc: 49.3.

- [Object Detection on PASCAL VOC 2007 (test)](<https://www.sota2.com/research/sota/object-detection-on-pascal-voc-2007-test.md>): Object Detection; PASCAL VOC 2007 (test); mAP: 31.3.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1703.01290>)

[PDF](<https://arxiv.org/pdf/1703.01290.pdf>)