# Weakly Supervised Object Detection with Segmentation Collaboration

arXiv: https://arxiv.org/abs/1904.00551 · HTML: https://mlsanity.com/paper/1904.00551/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 26 of 26)

- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection](https://mlsanity.com/paper/2207.03482/) (2022)
- [Open-Vocabulary Point-Cloud Object Detection without 3D Annotation](https://mlsanity.com/paper/2304.00788/) (2023)
- [3D Spatial Recognition without Spatially Labeled 3D](https://mlsanity.com/paper/2105.06461/) (2021)
- [Self-supervised object detection from audio-visual correspondence](https://mlsanity.com/paper/2104.06401/) (2021)
- [Group R-CNN for Weakly Semi-supervised Object Detection with Points](https://mlsanity.com/paper/2205.05920/) (2022)
- [CLIM: Contrastive Language-Image Mosaic for Region Representation](https://mlsanity.com/paper/2312.11376/) (2023)
- [Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters](https://mlsanity.com/paper/2108.01499/) (2021)
- [WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition](https://mlsanity.com/paper/2402.14812/) (2024)
- [Open-Vocabulary Object Detection via Language Hierarchy](https://mlsanity.com/paper/2410.20371/) (2024)
- [W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection](https://mlsanity.com/paper/2207.12104/) (2022)
- [Scaling Novel Object Detection with Weakly Supervised Detection Transformers](https://mlsanity.com/paper/2207.05205/) (2022)
- [Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey](https://mlsanity.com/paper/2105.12694/) (2021)
- [Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection](https://mlsanity.com/paper/2308.05991/) (2023)
- [PatchNet: Unsupervised Object Discovery based on Patch Embedding](https://mlsanity.com/paper/2106.08599/) (2021)
- [Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection](https://mlsanity.com/paper/2108.01785/) (2021)
- [Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection](https://mlsanity.com/paper/2110.07511/) (2021)
- [Weakly-semi-supervised object detection in remotely sensed imagery](https://mlsanity.com/paper/2311.17449/) (2023)
- [Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/2110.09060/) (2021)
- [Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images](https://mlsanity.com/paper/2112.05786/) (2021)
- [Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection](https://mlsanity.com/paper/2204.06899/) (2022)
- [Online progressive instance-balanced sampling for weakly supervised object detection](https://mlsanity.com/paper/2206.10324/) (2022)
- [Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information](https://mlsanity.com/paper/2304.14114/) (2023)
- [Lesion Detection on Leaves using Class Activation Maps](https://mlsanity.com/paper/2306.13366/) (2023)
- [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)
- [State and Scene Enhanced Prototypes for Weakly Supervised Open-Vocabulary Object Detection](https://mlsanity.com/paper/2511.18012/) (2025)

## Abstract (arXiv)

Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 51.0% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.