# Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning

arXiv: https://arxiv.org/abs/2002.08098 · HTML: https://mlsanity.com/paper/2002.08098/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 30 of 37)

- [Multi-class Token Transformer for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02891/) (2022)
- [Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02909/) (2022)
- [Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast](https://mlsanity.com/paper/2110.07110/) (2021)
- [Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2107.11787/) (2021)
- [Cross Language Image Matching for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02668/) (2022)
- [Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2103.01795/) (2021)
- [Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.09653/) (2022)
- [A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction](https://mlsanity.com/paper/2207.01223/) (2022)
- [Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2211.12268/) (2022)
- [Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.01344/) (2021)
- [Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.10278/) (2022)
- [Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling](https://mlsanity.com/paper/2202.04812/) (2022)
- [Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2206.09554/) (2022)
- [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)
- [Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05154/) (2023)
- [Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness](https://mlsanity.com/paper/2303.00971/) (2023)
- [USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.07806/) (2023)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)
- [Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2407.02768/) (2024)
- [Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference](https://mlsanity.com/paper/2205.04227/) (2022)
- [Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentation](https://mlsanity.com/paper/2410.01341/) (2024)
- [360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception](https://mlsanity.com/paper/2312.16268/) (2023)
- [WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations](https://mlsanity.com/paper/2107.03088/) (2021)
- [DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps](https://mlsanity.com/paper/2502.15885/) (2025)
- [Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2304.09059/) (2023)
- [Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2307.00097/) (2023)
- [Training Semantic Segmentation on Heterogeneous Datasets](https://mlsanity.com/paper/2301.07634/) (2023)
- [Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensator](https://mlsanity.com/paper/2309.00871/) (2023)
- [Weakly Supervised Semantic Segmentation by Knowledge Graph Inference](https://mlsanity.com/paper/2309.14057/) (2023)

## Abstract (arXiv)

Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. Recent methods have exploited classification networks to localize objects by selecting regions with strong response. While such response map provides sparse information, however, there exist strong pairwise relations between pixels in natural images, which can be utilized to propagate the sparse map to a much denser one. In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network. The refined results by the pairwise network are then used as supervision to train the unary network, and the procedures are conducted iteratively to obtain better segmentation progressively. To learn reliable pixel affinity without accurate annotation, we also propose to mine confident regions. We show that iteratively training this framework is equivalent to optimizing an energy function with convergence to a local minimum. Experimental results on the PASCAL VOC 2012 and COCO datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.