# Can Image-Level Labels Replace Pixel-Level Labels for Image Parsing

arXiv: https://arxiv.org/abs/1403.1626 · HTML: https://mlsanity.com/paper/1403.1626/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2014.

## Built on by (most cited first, 15 of 15)

- [Learning to Detect Instance-level Salient Objects Using Complementary Image Labels](https://mlsanity.com/paper/2111.10137/) (2021)
- [Learning to Rectify for Robust Learning with Noisy Labels](https://mlsanity.com/paper/2111.04239/) (2021)
- [Weakly Supervised Change Detection Using Guided Anisotropic Difusion](https://mlsanity.com/paper/2112.15367/) (2021)
- [How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model](https://mlsanity.com/paper/2312.10600/) (2023)
- [Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models](https://mlsanity.com/paper/2212.14306/) (2022)
- [Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic Segmentation](https://mlsanity.com/paper/2407.13254/) (2024)
- [Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation](https://mlsanity.com/paper/2103.11594/) (2021)
- [Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation](https://mlsanity.com/paper/2205.01777/) (2022)
- [Semi-Supervised Learning for Mars Imagery Classification and Segmentation](https://mlsanity.com/paper/2206.02180/) (2022)
- [Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception](https://mlsanity.com/paper/2112.13697/) (2021)
- [Joint Optical Neuroimaging Denoising with Semantic Tasks](https://mlsanity.com/paper/2109.10499/) (2021)
- [Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks](https://mlsanity.com/paper/2205.00160/) (2022)
- [Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection](https://mlsanity.com/paper/2301.10441/) (2023)
- [Understanding What Is Not Said:Referring Remote Sensing Image Segmentation with Scarce Expressions](https://mlsanity.com/paper/2510.22760/) (2025)
- [FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning](https://mlsanity.com/paper/2603.00517/) (2026)

## Abstract (arXiv)

This paper presents a weakly supervised sparse learning approach to the problem of noisily tagged image parsing, or segmenting all the objects within a noisily tagged image and identifying their categories (i.e. tags). Different from the traditional image parsing that takes pixel-level labels as strong supervisory information, our noisily tagged image parsing is provided with noisy tags of all the images (i.e. image-level labels), which is a natural setting for social image collections (e.g. Flickr). By oversegmenting all the images into regions, we formulate noisily tagged image parsing as a weakly supervised sparse learning problem over all the regions, where the initial labels of each region are inferred from image-level labels. Furthermore, we develop an efficient algorithm to solve such weakly supervised sparse learning problem. The experimental results on two benchmark datasets show the effectiveness of our approach. More notably, the reported surprising results shed some light on answering the question: can image-level labels replace pixel-level labels (hard to access) as supervisory information for image parsing.