# Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection

arXiv: https://arxiv.org/abs/2308.05991 · HTML: https://mlsanity.com/paper/2308.05991/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 54 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 30 of 33)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Unbiased Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2102.09480/) (2021)
- [Distilling Object Detectors with Fine-grained Feature Imitation](https://mlsanity.com/paper/1906.03609/) (2019)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [Distilling Object Detectors via Decoupled Features](https://mlsanity.com/paper/2103.14475/) (2021)
- [PCL: Proposal Cluster Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1807.03342/) (2018)
- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection](https://mlsanity.com/paper/2004.04725/) (2020)
- [C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1904.05647/) (2019)
- [WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection](https://mlsanity.com/paper/1909.04972/) (2019)
- [Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection](https://mlsanity.com/paper/2010.12023/) (2020)
- [Weakly Supervised Cascaded Convolutional Networks](https://mlsanity.com/paper/1611.08258/) (2016)
- [Min-Entropy Latent Model for Weakly Supervised Object Detection](https://mlsanity.com/paper/1902.06057/) (2019)
- [Towards Precise End-to-end Weakly Supervised Object Detection Network](https://mlsanity.com/paper/1911.12148/) (2019)
- [Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation](https://mlsanity.com/paper/2112.04840/) (2021)
- [ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization](https://mlsanity.com/paper/1609.04331/) (2016)
- [SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection](https://mlsanity.com/paper/2006.12884/) (2020)
- [Deep Self-Taught Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1704.05188/) (2017)
- [Weakly Supervised Object Detection with Segmentation Collaboration](https://mlsanity.com/paper/1904.00551/) (2019)
- [Zigzag Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1804.09466/) (2018)
- [Dissimilarity Coefficient based Weakly Supervised Object Detection](https://mlsanity.com/paper/1811.10016/) (2018)
- [TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection](https://mlsanity.com/paper/1807.04897/) (2018)
- [C-WSL: Count-guided Weakly Supervised Localization](https://mlsanity.com/paper/1711.05282/) (2017)
- [Object Instance Mining for Weakly Supervised Object Detection](https://mlsanity.com/paper/2002.01087/) (2020)
- [Object Discovery via Contrastive Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/2208.07576/) (2022)

## Built on by (most cited first, 4 of 4)

- [WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition](https://mlsanity.com/paper/2402.14812/) (2024)
- [Self-Classification Enhancement and Correction for Weakly Supervised Object Detection](https://mlsanity.com/paper/2505.16294/) (2025)
- [Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2507.02454/) (2025)
- [Dual-Thresholded Heatmap-Guided Proposal Clustering and Negative Certainty Supervision with Enhanced Base Network for Weakly Supervised Object Detection](https://mlsanity.com/paper/2509.08289/) (2025)

## Abstract (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. These inaccurate high-scoring region proposals will mislead the training of subsequent refinement modules and thus hamper the detection performance. In this work, we explore how to ameliorate the quality of pseudo-labeling in MIDN. Formally, we devise Cyclic-Bootstrap Labeling (CBL), a novel weakly supervised object detection pipeline, which optimizes MIDN with rank information from a reliable teacher network. Specifically, we obtain this teacher network by introducing a weighted exponential moving average strategy to take advantage of various refinement modules. A novel class-specific ranking distillation algorithm is proposed to leverage the output of weighted ensembled teacher network for distilling MIDN with rank information. As a result, MIDN is guided to assign higher scores to accurate proposals among their neighboring ones, thus benefiting the subsequent pseudo labeling. Extensive experiments on the prevalent PASCAL VOC 2007 & 2012 and COCO datasets demonstrate the superior performance of our CBL framework. Code will be available at https://github.com/Yinyf0804/WSOD-CBL/.