# One-Shot Object Affordance Detection in the Wild

arXiv: https://arxiv.org/abs/2108.03658 · HTML: https://mlsanity.com/paper/2108.03658/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 119 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 30 of 63)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [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)
- [Prior Guided Feature Enrichment Network for Few-Shot Segmentation](https://mlsanity.com/paper/2008.01449/) (2020)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)

## Built on by (most cited first, 30 of 40)

- [Learning Affordance Grounding from Exocentric Images](https://mlsanity.com/paper/2203.09905/) (2022)
- [Grounding 3D Object Affordance from 2D Interactions in Images](https://mlsanity.com/paper/2303.10437/) (2023)
- [Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces](https://mlsanity.com/paper/2503.19199/) (2025)
- [LEMON: Learning 3D Human-Object Interaction Relation from 2D Images](https://mlsanity.com/paper/2312.08963/) (2023)
- [Open-Vocabulary Affordance Detection in 3D Point Clouds](https://mlsanity.com/paper/2303.02401/) (2023)
- [Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks](https://mlsanity.com/paper/2212.05946/) (2022)
- [Affordance Grounding from Demonstration Video to Target Image](https://mlsanity.com/paper/2303.14644/) (2023)
- [GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding](https://mlsanity.com/paper/2411.19626/) (2024)
- [EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views](https://mlsanity.com/paper/2405.13659/) (2024)
- [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022)
- [Deep Graph Reprogramming](https://mlsanity.com/paper/2304.14593/) (2023)
- [Phrase-Based Affordance Detection via Cyclic Bilateral Interaction](https://mlsanity.com/paper/2202.12076/) (2022)
- [Multi-label affordance mapping from egocentric vision](https://mlsanity.com/paper/2309.02120/) (2023)
- [Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation](https://mlsanity.com/paper/2309.12943/) (2023)
- [Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention](https://mlsanity.com/paper/2405.18295/) (2024)
- [Discovering Human-Object Interaction Concepts via Self-Compositional Learning](https://mlsanity.com/paper/2203.14272/) (2022)
- [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)
- [Grounding 3D Scene Affordance From Egocentric Interactions](https://mlsanity.com/paper/2409.19650/) (2024)
- [PEAR: Phrase-Based Hand-Object Interaction Anticipation](https://mlsanity.com/paper/2407.21510/) (2024)
- [Text-driven Affordance Learning from Egocentric Vision](https://mlsanity.com/paper/2404.02523/) (2024)
- [2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos](https://mlsanity.com/paper/2503.09320/) (2025)
- [Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions](https://mlsanity.com/paper/2402.11265/) (2024)
- [MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye tracking](https://mlsanity.com/paper/2404.12083/) (2024)
- [Spatial-Aware Token for Weakly Supervised Object Localization](https://mlsanity.com/paper/2303.10438/) (2023)
- [Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark](https://mlsanity.com/paper/2305.16378/) (2023)
- [AffordanceSAM: Segment Anything Once More in Affordance Grounding](https://mlsanity.com/paper/2504.15650/) (2025)
- [One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes](https://mlsanity.com/paper/2503.01092/) (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)

## Abstract (arXiv)

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we first study 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 Network (OSAD-Net) that firstly estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OSAD-Net 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 large-scale Purpose-driven Affordance Dataset v2 (PADv2) by collecting and labeling 30k images from 39 affordance and 103 object categories. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation. Specifically, we conducted comprehensive experiments on PADv2 dataset by including 11 advanced models from several related research fields. 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 available at https://github.com/lhc1224/OSAD Net.