# Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization

arXiv: https://arxiv.org/abs/2008.01201 · HTML: https://mlsanity.com/paper/2008.01201/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 24 of 24)

- [Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2103.08896/) (2021)
- [Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2110.06530/) (2021)
- [L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2204.03206/) (2022)
- [Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2209.07761/) (2022)
- [Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2203.02172/) (2022)
- [A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective](https://mlsanity.com/paper/2208.09913/) (2022)
- [TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.07239/) (2022)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling](https://mlsanity.com/paper/2202.04812/) (2022)
- [Weakly Supervised Semantic Segmentation via Progressive Patch Learning](https://mlsanity.com/paper/2209.07828/) (2022)
- [Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization](https://mlsanity.com/paper/2204.04890/) (2022)
- [Weakly-supervised Medical Image Segmentation with Gaze Annotations](https://mlsanity.com/paper/2407.07406/) (2024)
- [Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2110.14309/) (2021)
- [GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation](https://mlsanity.com/paper/2112.02841/) (2021)
- [SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness](https://mlsanity.com/paper/2108.09929/) (2021)
- [Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2205.13092/) (2022)
- [Self-Improving Semantic Perception for Indoor Localisation](https://mlsanity.com/paper/2105.01595/) (2021)
- [ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation](https://mlsanity.com/paper/2311.17121/) (2023)
- [Dual Progressive Transformations for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2209.15211/) (2022)
- [All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.04321/) (2023)
- [Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization](https://mlsanity.com/paper/2205.00400/) (2022)
- [Localized Region Guidance for Class Activation Mapping in WSSS](https://mlsanity.com/paper/2509.12496/) (2025)
- [Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning](https://mlsanity.com/paper/2309.04148/) (2023)
- [Lightweight Transformer Framework for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2511.19765/) (2025)

## Abstract (arXiv)

Obtaining object response maps is one important step to achieve weakly-supervised semantic segmentation using image-level labels. However, existing methods rely on the classification task, which could result in a response map only attending on discriminative object regions as the network does not need to see the entire object for optimizing the classification loss. To tackle this issue, we propose a principled and end-to-end train-able framework to allow the network to pay attention to other parts of the object, while producing a more complete and uniform response map. Specifically, we introduce the mixup data augmentation scheme into the classification network and design two uncertainty regularization terms to better interact with the mixup strategy. In experiments, we conduct extensive analysis to demonstrate the proposed method and show favorable performance against state-of-the-art approaches.