# Adaptive Binarization for Weakly Supervised Affordance Segmentation

arXiv: https://arxiv.org/abs/1707.02850 · HTML: https://mlsanity.com/paper/1707.02850/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 16 of 16)

- [Learning Affordance Grounding from Exocentric Images](https://mlsanity.com/paper/2203.09905/) (2022)
- [LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2303.09665/) (2023)
- [One-Shot Open Affordance Learning with Foundation Models](https://mlsanity.com/paper/2311.17776/) (2023)
- [One-Shot Object Affordance Detection in the Wild](https://mlsanity.com/paper/2108.03658/) (2021)
- [Learning Precise Affordances from Egocentric Videos for Robotic Manipulation](https://mlsanity.com/paper/2408.10123/) (2024)
- [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022)
- [Phrase-Based Affordance Detection via Cyclic Bilateral Interaction](https://mlsanity.com/paper/2202.12076/) (2022)
- [STRAP: Structured Object Affordance Segmentation with Point Supervision](https://mlsanity.com/paper/2304.08492/) (2023)
- [What does CLIP know about peeling a banana?](https://mlsanity.com/paper/2404.12015/) (2024)
- [DAViD: Modeling Dynamic Affordance of 3D Objects Using Pre-trained Video Diffusion Models](https://mlsanity.com/paper/2501.08333/) (2025)
- [Multi-Keypoint Affordance Representation for Functional Dexterous Grasping](https://mlsanity.com/paper/2502.20018/) (2025)
- [Closed-Loop Transfer for Weakly-supervised Affordance Grounding](https://mlsanity.com/paper/2510.17384/) (2025)
- [Visual-Geometric Collaborative Guidance for Affordance Learning](https://mlsanity.com/paper/2410.11363/) (2024)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [Leverage Task Context for Object Affordance Ranking](https://mlsanity.com/paper/2411.16082/) (2024)
- [Zero-shot 2D Grounding with Novel Affordance Types](https://mlsanity.com/paper/2608.08929/) (2026)

## Abstract (arXiv)

The concept of affordance is important to understand the relevance of object parts for a certain functional interaction. Affordance types generalize across object categories and are not mutually exclusive. This makes the segmentation of affordance regions of objects in images a difficult task. In this work, we build on an iterative approach that learns a convolutional neural network for affordance segmentation from sparse keypoints. During this process, the predictions of the network need to be binarized. In this work, we propose an adaptive approach for binarization and estimate the parameters for initialization by approximated cross validation. We evaluate our approach on two affordance datasets where our approach outperforms the state-of-the-art for weakly supervised affordance segmentation.