# Background Activation Suppression for Weakly Supervised Object Localization arXiv: https://arxiv.org/abs/2112.00580 ยท HTML: https://mlsanity.com/paper/2112.00580/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 35 references that are in the map. - First posted on arXiv 1 Dec 2021. ## Builds on (most cited first, 25 of 25) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017) - [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) - [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021) - [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) - [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019) - [TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.14862/) (2021) - [Evaluating Weakly Supervised Object Localization Methods Right](https://mlsanity.com/paper/2001.07437/) (2020) - [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (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) - [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) - [Shallow Feature Matters for Weakly Supervised Object Localization](https://mlsanity.com/paper/2108.00873/) (2021) - [Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2110.05741/) (2021) - [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) - [Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps](https://mlsanity.com/paper/2006.05220/) (2020) ## Built on by (most cited first, 22 of 22) - [Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision](https://mlsanity.com/paper/2301.09121/) (2023) - [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022) - [Generative Prompt Model for Weakly Supervised Object Localization](https://mlsanity.com/paper/2307.09756/) (2023) - [Phrase-Based Affordance Detection via Cyclic Bilateral Interaction](https://mlsanity.com/paper/2202.12076/) (2022) - [Object Localization under Single Coarse Point Supervision](https://mlsanity.com/paper/2203.09338/) (2022) - [Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation](https://mlsanity.com/paper/2309.12943/) (2023) - [Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization](https://mlsanity.com/paper/2209.09209/) (2022) - [What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs](https://mlsanity.com/paper/2206.09358/) (2022) - [Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification](https://mlsanity.com/paper/2304.01804/) (2023) - [Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology](https://mlsanity.com/paper/2404.19113/) (2024) - [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) - [Knowledge-guided Causal Intervention for Weakly-supervised Object Localization](https://mlsanity.com/paper/2301.01060/) (2023) - [Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization](https://mlsanity.com/paper/2305.15354/) (2023) - [Unsupervised Object Localization with Representer Point Selection](https://mlsanity.com/paper/2309.04172/) (2023) - [FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization](https://mlsanity.com/paper/2309.09122/) (2023) - [Open-World Weakly-Supervised Object Localization](https://mlsanity.com/paper/2304.08271/) (2023) - [Rethinking the Localization in Weakly Supervised Object Localization](https://mlsanity.com/paper/2308.06161/) (2023) - [Quantifying Context Bias in Domain Adaptation for Object Detection](https://mlsanity.com/paper/2409.14679/) (2024) - [TeD-Loc: Text Distillation for Weakly Supervised Object Localization](https://mlsanity.com/paper/2501.12632/) (2025) - [Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization](https://mlsanity.com/paper/2505.04905/) (2025) - [Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling](https://mlsanity.com/paper/2505.18446/) (2025) ## Abstract (arXiv) Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels. Recently a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve localization task. Existing FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction map and to guide the learning of generator. We argue for using activation value to achieve more efficient learning. It is based on the experimental observation that, for a trained network, CE converges to zero when the foreground mask covers only part of the object region. While activation value increases until the mask expands to the object boundary, which indicates that more object areas can be learned by using activation value. In this paper, we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint module (AMC) is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using the foreground region guidance and the area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets. Code and models are available at https://github.com/wpy1999/BAS.