# Weakly Supervised Object Localization as Domain Adaption arXiv: https://arxiv.org/abs/2203.01714 ยท HTML: https://mlsanity.com/paper/2203.01714/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 33 references that are in the map. - First posted on arXiv 3 Mar 2022. ## Builds on (most cited first, 22 of 22) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019) - [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015) - [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014) - [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016) - [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013) - [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019) - [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017) - [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018) - [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018) - [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019) - [Deep Subdomain Adaptation Network for Image Classification](https://mlsanity.com/paper/2106.09388/) (2021) - [Self-produced Guidance for Weakly-supervised Object Localization](https://mlsanity.com/paper/1807.08902/) (2018) - [Inter-Image Communication for Weakly Supervised Localization](https://mlsanity.com/paper/2008.05096/) (2020) - [Rethinking the Route Towards Weakly Supervised Object Localization](https://mlsanity.com/paper/2002.11359/) (2020) - [Transfer Learning with Dynamic Adversarial Adaptation Network](https://mlsanity.com/paper/1909.08184/) (2019) - [Geometry Constrained Weakly Supervised Object Localization](https://mlsanity.com/paper/2007.09727/) (2020) - [Normalization Matters in Weakly Supervised Object Localization](https://mlsanity.com/paper/2107.13221/) (2021) - [In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization](https://mlsanity.com/paper/2009.12063/) (2020) - [Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps](https://mlsanity.com/paper/2006.05220/) (2020) ## Built on by (most cited first, 16 of 16) - [MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.03005/) (2023) - [Generative Prompt Model for Weakly Supervised Object Localization](https://mlsanity.com/paper/2307.09756/) (2023) - [On the Benefits of Attribute-Driven Graph Domain Adaptation](https://mlsanity.com/paper/2502.06808/) (2025) - [Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation](https://mlsanity.com/paper/2309.12943/) (2023) - [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024) - [Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization](https://mlsanity.com/paper/2207.07818/) (2022) - [Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology](https://mlsanity.com/paper/2404.19113/) (2024) - [Spatial-Aware Token for Weakly Supervised Object Localization](https://mlsanity.com/paper/2303.10438/) (2023) - [Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label](https://mlsanity.com/paper/2402.17555/) (2024) - [Unsupervised Object Localization with Representer Point Selection](https://mlsanity.com/paper/2309.04172/) (2023) - [Open-World Weakly-Supervised Object Localization](https://mlsanity.com/paper/2304.08271/) (2023) - [Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label](https://mlsanity.com/paper/2404.09475/) (2024) - [Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2408.02039/) (2024) - [Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.04949/) (2023) - [BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications](https://mlsanity.com/paper/2309.03509/) (2023) - [TriLite: Efficient Weakly Supervised Object Localization with Universal Visual Features and Tri-Region Disentanglement](https://mlsanity.com/paper/2602.23120/) (2026) ## Abstract (arXiv) Weakly supervised object localization (WSOL) focuses on localizing objects only with the supervision of image-level classification masks. Most previous WSOL methods follow the classification activation map (CAM) that localizes objects based on the classification structure with the multi-instance learning (MIL) mechanism. However, the MIL mechanism makes CAM only activate discriminative object parts rather than the whole object, weakening its performance for localizing objects. To avoid this problem, this work provides a novel perspective that models WSOL as a domain adaption (DA) task, where the score estimator trained on the source/image domain is tested on the target/pixel domain to locate objects. Under this perspective, a DA-WSOL pipeline is designed to better engage DA approaches into WSOL to enhance localization performance. It utilizes a proposed target sampling strategy to select different types of target samples. Based on these types of target samples, domain adaption localization (DAL) loss is elaborated. It aligns the feature distribution between the two domains by DA and makes the estimator perceive target domain cues by Universum regularization. Experiments show that our pipeline outperforms SOTA methods on multi benchmarks. Code are released at urlhttps://github.com/zh460045050/DA-WSOLCVPR2022.