# One-Shot Affordance Detection

arXiv: https://arxiv.org/abs/2106.14747 · HTML: https://mlsanity.com/paper/2106.14747/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Expectation-Maximization Attention Networks for Semantic Segmentation](https://mlsanity.com/paper/1907.13426/) (2019)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [Highly Efficient Salient Object Detection with 100K Parameters](https://mlsanity.com/paper/2003.05643/) (2020)
- [Learning to Act Properly: Predicting and Explaining Affordances from Images](https://mlsanity.com/paper/1712.07576/) (2017)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [On Exploring Undetermined Relationships for Visual Relationship Detection](https://mlsanity.com/paper/1905.01595/) (2019)
- [Visual Affordance and Function Understanding: A Survey](https://mlsanity.com/paper/1807.06775/) (2018)
- [Progressive One-shot Human Parsing](https://mlsanity.com/paper/2012.11810/) (2020)
- [One-Shot Segmentation in Clutter](https://mlsanity.com/paper/1803.09597/) (2018)
- [One-Shot Texture Retrieval with Global Context Metric](https://mlsanity.com/paper/1905.06656/) (2019)

## Built on by (most cited first, 30 of 32)

- [Grounding 3D Object Affordance from 2D Interactions in Images](https://mlsanity.com/paper/2303.10437/) (2023)
- [One-Shot Open Affordance Learning with Foundation Models](https://mlsanity.com/paper/2311.17776/) (2023)
- [Open-Vocabulary Affordance Detection in 3D Point Clouds](https://mlsanity.com/paper/2303.02401/) (2023)
- [One-Shot Object Affordance Detection in the Wild](https://mlsanity.com/paper/2108.03658/) (2021)
- [One-Shot Transfer of Affordance Regions? AffCorrs!](https://mlsanity.com/paper/2209.07147/) (2022)
- [GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding](https://mlsanity.com/paper/2411.19626/) (2024)
- [Learning Visual Affordance Grounding from Demonstration Videos](https://mlsanity.com/paper/2108.05675/) (2021)
- [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022)
- [Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding](https://mlsanity.com/paper/2408.13024/) (2024)
- [Phrase-Based Affordance Detection via Cyclic Bilateral Interaction](https://mlsanity.com/paper/2202.12076/) (2022)
- [Grounding 3D Object Affordance with Language Instructions, Visual Observations and Interactions](https://mlsanity.com/paper/2504.04744/) (2025)
- [GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency](https://mlsanity.com/paper/2412.09511/) (2024)
- [Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention](https://mlsanity.com/paper/2405.18295/) (2024)
- [RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments](https://mlsanity.com/paper/2310.17290/) (2023)
- [WorldAfford: Affordance Grounding based on Natural Language Instructions](https://mlsanity.com/paper/2405.12461/) (2024)
- [GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation](https://mlsanity.com/paper/2505.11865/) (2025)
- [Grounding 3D Scene Affordance From Egocentric Interactions](https://mlsanity.com/paper/2409.19650/) (2024)
- [Visual Intention Grounding for Egocentric Assistants](https://mlsanity.com/paper/2504.13621/) (2025)
- [Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors](https://mlsanity.com/paper/2505.24103/) (2025)
- [NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities](https://mlsanity.com/paper/2311.01454/) (2023)
- [Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions](https://mlsanity.com/paper/2402.11265/) (2024)
- [Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision](https://mlsanity.com/paper/2506.06253/) (2025)
- [Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation](https://mlsanity.com/paper/2503.03556/) (2025)
- [Beyond Object Categories: Multi-Attribute Reference Understanding for Visual Grounding](https://mlsanity.com/paper/2503.19240/) (2025)
- [Visual-Geometric Collaborative Guidance for Affordance Learning](https://mlsanity.com/paper/2410.11363/) (2024)
- [Unlocking 3D Affordance Segmentation with 2D Semantic Knowledge](https://mlsanity.com/paper/2510.08316/) (2025)
- [Implicit Affordance Acquisition via Causal Action-Effect Modeling in the Video Domain](https://mlsanity.com/paper/2312.11345/) (2023)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [Populate-A-Scene: Affordance-Aware Human Video Generation](https://mlsanity.com/paper/2507.00334/) (2025)
- [Leverage Task Context for Object Affordance Ranking](https://mlsanity.com/paper/2411.16082/) (2024)

## Abstract (arXiv)

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OS-AD can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k images from 31 affordance and 72 object categories. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is at ProjectPage.