# Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization arXiv: https://arxiv.org/abs/2207.07818 ยท HTML: https://mlsanity.com/paper/2207.07818/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 41 references that are in the map. - First posted on arXiv 16 Jul 2022. ## Builds on (most cited first, 28 of 28) - [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) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [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) - [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017) - [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017) - [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019) - [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017) - [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018) - [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020) - [Large-scale interactive object segmentation with human annotators](https://mlsanity.com/paper/1903.10830/) (2019) - [Evaluating Weakly Supervised Object Localization Methods Right](https://mlsanity.com/paper/2001.07437/) (2020) - [CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1811.10842/) (2018) - [Self-produced Guidance for Weakly-supervised Object Localization](https://mlsanity.com/paper/1807.08902/) (2018) - [C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1904.05647/) (2019) - [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) - [Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.04523/) (2021) - [Geometry Constrained Weakly Supervised Object Localization](https://mlsanity.com/paper/2007.09727/) (2020) - [Combinational Class Activation Maps for Weakly Supervised Object Localization](https://mlsanity.com/paper/1910.05518/) (2019) - [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) - [Weakly Supervised Object Localization as Domain Adaption](https://mlsanity.com/paper/2203.01714/) (2022) - [Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps](https://mlsanity.com/paper/2006.05220/) (2020) - [EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement](https://mlsanity.com/paper/2002.07421/) (2020) - [Unifying Nonlocal Blocks for Neural Networks](https://mlsanity.com/paper/2108.02451/) (2021) ## Built on by (most cited first, 9 of 9) - [MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.03005/) (2023) - [Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization](https://mlsanity.com/paper/2209.09209/) (2022) - [DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization](https://mlsanity.com/paper/2310.06196/) (2023) - [Spatial-Aware Token for Weakly Supervised Object Localization](https://mlsanity.com/paper/2303.10438/) (2023) - [Unsupervised Object Localization with Representer Point Selection](https://mlsanity.com/paper/2309.04172/) (2023) - [Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensator](https://mlsanity.com/paper/2309.00871/) (2023) - [BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications](https://mlsanity.com/paper/2309.03509/) (2023) - [Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization](https://mlsanity.com/paper/2505.04905/) (2025) - [Inter- and Intra-image Refinement for Few Shot Segmentation](https://mlsanity.com/paper/2507.05838/) (2025) ## Abstract (arXiv) Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL). However, CAM directly uses the classifier trained on image-level features to locate objects, making it prefers to discern global discriminative factors rather than regional object cues. Thus only the discriminative locations are activated when feeding pixel-level features into this classifier. To solve this issue, this paper elaborates a plug-and-play mechanism called BagCAMs to better project a well-trained classifier for the localization task without refining or re-training the baseline structure. Our BagCAMs adopts a proposed regional localizer generation (RLG) strategy to define a set of regional localizers and then derive them from a well-trained classifier. These regional localizers can be viewed as the base learner that only discerns region-wise object factors for localization tasks, and their results can be effectively weighted by our BagCAMs to form the final localization map. Experiments indicate that adopting our proposed BagCAMs can improve the performance of baseline WSOL methods to a great extent and obtains state-of-the-art performance on three WSOL benchmarks. Code are released at https://github.com/zh460045050/BagCAMs.