# One-Shot Transfer of Affordance Regions? AffCorrs!

arXiv: https://arxiv.org/abs/2209.07147 · HTML: https://mlsanity.com/paper/2209.07147/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 27 of 27)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [Feature Weighting and Boosting for Few-Shot Segmentation](https://mlsanity.com/paper/1909.13140/) (2019)
- [Self-Supervised Representation Learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2110.09327/) (2021)
- [Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation](https://mlsanity.com/paper/1806.08756/) (2018)
- [Few-Shot Segmentation via Cycle-Consistent Transformer](https://mlsanity.com/paper/2106.02320/) (2021)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [Learning What Not to Segment: A New Perspective on Few-Shot Segmentation](https://mlsanity.com/paper/2203.07615/) (2022)
- [SCOPS: Self-Supervised Co-Part Segmentation](https://mlsanity.com/paper/1905.01298/) (2019)
- [NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields](https://mlsanity.com/paper/2203.01913/) (2022)
- [Unsupervised Part Discovery from Contrastive Reconstruction](https://mlsanity.com/paper/2111.06349/) (2021)
- [CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation](https://mlsanity.com/paper/2109.09163/) (2021)
- [One-Shot Instance Segmentation](https://mlsanity.com/paper/1811.11507/) (2018)
- [Warp Consistency for Unsupervised Learning of Dense Correspondences](https://mlsanity.com/paper/2104.03308/) (2021)
- [One-Shot Affordance Detection](https://mlsanity.com/paper/2106.14747/) (2021)
- [Affordance Learning from Play for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2203.00352/) (2022)
- [Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1710.00290/) (2017)
- [Learning 3D Dense Correspondence via Canonical Point Autoencoder](https://mlsanity.com/paper/2107.04867/) (2021)
- [Unsupervised Co-part Segmentation through Assembly](https://mlsanity.com/paper/2106.05897/) (2021)
- [Learning Contrastive Representation for Semantic Correspondence](https://mlsanity.com/paper/2109.10967/) (2021)
- [Learning Semantic Correspondence Exploiting an Object-level Prior](https://mlsanity.com/paper/1911.12914/) (2019)
- [Fully Self-Supervised Class Awareness in Dense Object Descriptors](https://mlsanity.com/paper/2110.01957/) (2021)
- [Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics](https://mlsanity.com/paper/1611.00201/) (2016)
- [Visual Descriptor Learning from Monocular Video](https://mlsanity.com/paper/2004.07007/) (2020)

## Built on by (most cited first, 30 of 31)

- [LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2303.09665/) (2023)
- [AffordanceLLM: Grounding Affordance from Vision Language Models](https://mlsanity.com/paper/2401.06341/) (2024)
- [Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction](https://mlsanity.com/paper/2409.18121/) (2024)
- [DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models](https://mlsanity.com/paper/2402.13181/) (2024)
- [Open-Vocabulary Affordance Detection in 3D Point Clouds](https://mlsanity.com/paper/2303.02401/) (2023)
- [Dexterous Functional Grasping](https://mlsanity.com/paper/2312.02975/) (2023)
- [SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks](https://mlsanity.com/paper/2307.03567/) (2023)
- [One-Shot Imitation Learning: A Pose Estimation Perspective](https://mlsanity.com/paper/2310.12077/) (2023)
- [GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency](https://mlsanity.com/paper/2412.09511/) (2024)
- [Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks](https://mlsanity.com/paper/2403.03270/) (2024)
- [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)
- [INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2409.06210/) (2024)
- [Mobi-π: Mobilizing Your Robot Learning Policy](https://mlsanity.com/paper/2505.23692/) (2025)
- [Seeing the Unseen: Visual Common Sense for Semantic Placement](https://mlsanity.com/paper/2401.07770/) (2024)
- [2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos](https://mlsanity.com/paper/2503.09320/) (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)
- [Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale](https://mlsanity.com/paper/2506.12009/) (2025)
- [Text-Guided Vector Graphics Customization](https://mlsanity.com/paper/2309.12302/) (2023)
- [AffordVLA: Injecting Affordance Representations into Vision-Language-Action Models via Implicit Feature Alignment](https://mlsanity.com/paper/2605.17517/) (2026)
- [Self-Explainable Affordance Learning with Embodied Caption](https://mlsanity.com/paper/2404.05603/) (2024)
- [Gradient-Weighted Feature Back-Projection: A Fast Alternative to Feature Distillation in 3D Gaussian Splatting](https://mlsanity.com/paper/2411.15193/) (2024)
- [Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features](https://mlsanity.com/paper/2501.17594/) (2025)
- [Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement](https://mlsanity.com/paper/2403.14000/) (2024)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT](https://mlsanity.com/paper/2309.08134/) (2023)
- [Adapting Skills to Novel Grasps: A Self-Supervised Approach](https://mlsanity.com/paper/2408.00178/) (2024)
- [TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization](https://mlsanity.com/paper/2509.00310/) (2025)
- [RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment](https://mlsanity.com/paper/2603.29419/) (2026)

## Abstract (arXiv)

In this work, we tackle one-shot visual search of object parts. Given a single reference image of an object with annotated affordance regions, we segment semantically corresponding parts within a target scene. We propose AffCorrs, an unsupervised model that combines the properties of pre-trained DINO-ViT's image descriptors and cyclic correspondences. We use AffCorrs to find corresponding affordances both for intra- and inter-class one-shot part segmentation. This task is more difficult than supervised alternatives, but enables future work such as learning affordances via imitation and assisted teleoperation.