# Budget-aware Semi-Supervised Semantic and Instance Segmentation

arXiv: https://arxiv.org/abs/1905.05880 · HTML: https://mlsanity.com/paper/1905.05880/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 12 of 12)

- [BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation](https://mlsanity.com/paper/2103.08907/) (2021)
- [Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement](https://mlsanity.com/paper/2109.09477/) (2021)
- [The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation](https://mlsanity.com/paper/2303.15062/) (2023)
- [Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.13393/) (2021)
- [Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings](https://mlsanity.com/paper/2103.14572/) (2021)
- [BoxMask: Revisiting Bounding Box Supervision for Video Object Detection](https://mlsanity.com/paper/2210.06008/) (2022)
- [Generalized Class Discovery in Instance Segmentation](https://mlsanity.com/paper/2502.08149/) (2025)
- [Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation](https://mlsanity.com/paper/2107.04523/) (2021)
- [Incremental Learning on Food Instance Segmentation](https://mlsanity.com/paper/2306.15910/) (2023)
- [Hybrid supervised instance segmentation by learning label noise suppression](https://mlsanity.com/paper/2610.20412/) (2026)
- [How Low Can We Go? Pixel Annotation for Semantic Segmentation](https://mlsanity.com/paper/2201.10448/) (2022)
- [Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video](https://mlsanity.com/paper/2512.16977/) (2025)

## Abstract (arXiv)

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.