# Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?

arXiv: https://arxiv.org/abs/2107.02095 · HTML: https://mlsanity.com/paper/2107.02095/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes](https://mlsanity.com/paper/2012.02924/) (2020)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [Do Transformer Modifications Transfer Across Implementations and Applications?](https://mlsanity.com/paper/2102.11972/) (2021)
- [Learning to Act Properly: Predicting and Explaining Affordances from Images](https://mlsanity.com/paper/1712.07576/) (2017)
- [Face Detection with the Faster R-CNN](https://mlsanity.com/paper/1606.03473/) (2016)
- [Affordances in Robotic Tasks -- A Survey](https://mlsanity.com/paper/2004.07400/) (2020)
- [Affordance Learning for End-to-End Visuomotor Robot Control](https://mlsanity.com/paper/1903.04053/) (2019)
- [Stronger Baselines for Trustable Results in Neural Machine Translation](https://mlsanity.com/paper/1706.09733/) (2017)
- [Physical Primitive Decomposition](https://mlsanity.com/paper/1809.05070/) (2018)

## Built on by (most cited first, 6 of 6)

- [Multi-label affordance mapping from egocentric vision](https://mlsanity.com/paper/2309.02120/) (2023)
- [Bayesian Deep Learning for Affordance Segmentation in images](https://mlsanity.com/paper/2303.00871/) (2023)
- [Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors](https://mlsanity.com/paper/2505.24103/) (2025)
- [Segmenting Object Affordances: Reproducibility and Sensitivity to Scale](https://mlsanity.com/paper/2409.01814/) (2024)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers](https://mlsanity.com/paper/2605.03614/) (2026)

## Abstract (arXiv)

Affordances are the possibilities of actions the environment offers to the individual. Ordinary objects (hammer, knife) usually have many affordances (grasping, pounding, cutting), and detecting these allow artificial agents to understand what are their possibilities in the environment, with obvious application in Robotics. Proposed benchmarks and state-of-the-art prediction models for supervised affordance segmentation are usually modifications of popular object segmentation models such as Mask R-CNN. We observe that theoretically, these popular object segmentation methods should be sufficient for detecting affordances masks. So we ask the question: is it necessary to tailor new architectures to the problem of learning affordances? We show that applying the out-of-the-box Mask R-CNN to the problem of affordances segmentation outperforms the current state-of-the-art. We conclude that the problem of supervised affordance segmentation is included in the problem of object segmentation and argue that better benchmarks for affordance learning should include action capacities.