# L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation

arXiv: https://arxiv.org/abs/2204.03206 · HTML: https://mlsanity.com/paper/2204.03206/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 66 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 30 of 50)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Born Again Neural Networks](https://mlsanity.com/paper/1805.04770/) (2018)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Structured Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/1903.04197/) (2019)
- [Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations](https://mlsanity.com/paper/1904.05044/) (2019)
- [BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1503.01640/) (2015)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach](https://mlsanity.com/paper/1703.08448/) (2017)
- [Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation](https://mlsanity.com/paper/1603.06098/) (2016)
- [Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation](https://mlsanity.com/paper/1502.02734/) (2015)
- [Wider or Deeper: Revisiting the ResNet Model for Visual Recognition](https://mlsanity.com/paper/1611.10080/) (2016)
- [Tell Me Where to Look: Guided Attention Inference Network](https://mlsanity.com/paper/1802.10171/) (2018)
- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)
- [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018)
- [FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference](https://mlsanity.com/paper/1902.10421/) (2019)
- [Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation](https://mlsanity.com/paper/1805.04574/) (2018)
- [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020)

## Built on by (most cited first, 30 of 57)

- [CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2212.09506/) (2022)
- [Token Contrast for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.01267/) (2023)
- [Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05803/) (2023)
- [A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction](https://mlsanity.com/paper/2207.01223/) (2022)
- [CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2306.04300/) (2023)
- [Self-Calibrated Cross Attention Network for Few-Shot Segmentation](https://mlsanity.com/paper/2308.09294/) (2023)
- [Extracting Class Activation Maps from Non-Discriminative Features as well](https://mlsanity.com/paper/2303.10334/) (2023)
- [Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.01275/) (2023)
- [Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2211.12268/) (2022)
- [WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/2304.01184/) (2023)
- [MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.03005/) (2023)
- [An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems](https://mlsanity.com/paper/2305.01586/) (2023)
- [Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models](https://mlsanity.com/paper/2310.13026/) (2023)
- [Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation](https://mlsanity.com/paper/2309.12943/) (2023)
- [Spatial Structure Constraints for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2401.11122/) (2024)
- [DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2403.11184/) (2024)
- [Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05154/) (2023)
- [Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Models](https://mlsanity.com/paper/2311.17095/) (2023)
- [Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2406.11189/) (2024)
- [USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.07806/) (2023)
- [Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2402.18467/) (2024)
- [MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2304.09913/) (2023)
- [Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2407.02768/) (2024)
- [Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2503.20826/) (2025)
- [Centered Self-Attention Layers](https://mlsanity.com/paper/2306.01610/) (2023)
- [Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models](https://mlsanity.com/paper/2212.14306/) (2022)
- [Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video](https://mlsanity.com/paper/2407.15794/) (2024)
- [Dual Progressive Transformations for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2209.15211/) (2022)
- [Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2404.08195/) (2024)
- [Fine-grained Background Representation for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2406.15755/) (2024)

## Abstract (arXiv)

Mining precise class-aware attention maps, a.k.a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. We observe that classification models can discover object regions with more details when replacing the input image with its local patches. Taking this into account, we first leverage a local classification network to extract attentions from multiple local patches randomly cropped from the input image. Then, we utilize a global network to learn complementary attention knowledge across multiple local attention maps online. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014, respectively, setting new state-of-the-art records. Code is available at https://github.com/PengtaoJiang/L2G.