# OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features

arXiv: https://arxiv.org/abs/2003.06800 · HTML: https://mlsanity.com/paper/2003.06800/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 30 of 35)

- [Simple Open-Vocabulary Object Detection with Vision Transformers](https://mlsanity.com/paper/2205.06230/) (2022)
- [MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare](https://mlsanity.com/paper/2212.06870/) (2022)
- [Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images](https://mlsanity.com/paper/2204.10776/) (2022)
- [OSOP: A Multi-Stage One Shot Object Pose Estimation Framework](https://mlsanity.com/paper/2203.15533/) (2022)
- [Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment](https://mlsanity.com/paper/2104.07719/) (2021)
- [Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/2112.09791/) (2021)
- [Multi-Modal Classifiers for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2306.05493/) (2023)
- [Hallucination Improves Few-Shot Object Detection](https://mlsanity.com/paper/2105.01294/) (2021)
- [Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation](https://mlsanity.com/paper/2405.17859/) (2024)
- [GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting](https://mlsanity.com/paper/2403.10683/) (2024)
- [Semantic-aligned Fusion Transformer for One-shot Object Detection](https://mlsanity.com/paper/2203.09093/) (2022)
- [LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation](https://mlsanity.com/paper/2211.16290/) (2022)
- [Mobile Robot Manipulation using Pure Object Detection](https://mlsanity.com/paper/2201.12437/) (2022)
- [VoxDet: Voxel Learning for Novel Instance Detection](https://mlsanity.com/paper/2305.17220/) (2023)
- [A Survey of Deep Learning for Low-Shot Object Detection](https://mlsanity.com/paper/2112.02814/) (2021)
- [Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition](https://mlsanity.com/paper/2406.10712/) (2024)
- [Solving Instance Detection from an Open-World Perspective](https://mlsanity.com/paper/2503.00359/) (2025)
- [Towards Content-based Pixel Retrieval in Revisited Oxford and Paris](https://mlsanity.com/paper/2309.05438/) (2023)
- [CAT: Cross-Attention Transformer for One-Shot Object Detection](https://mlsanity.com/paper/2104.14984/) (2021)
- [An Improved Deep Learning Approach For Product Recognition on Racks in Retail Stores](https://mlsanity.com/paper/2202.13081/) (2022)
- [Low-shot Object Learning with Mutual Exclusivity Bias](https://mlsanity.com/paper/2312.03533/) (2023)
- [Learning Gaussian Data Augmentation in Feature Space for One-shot Object Detection in Manga](https://mlsanity.com/paper/2410.05935/) (2024)
- [Novel 3D Scene Understanding Applications From Recurrence in a Single Image](https://mlsanity.com/paper/2210.07991/) (2022)
- [VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts](https://mlsanity.com/paper/2304.00746/) (2023)
- [Find your Needle: Small Object Image Retrieval via Multi-Object Attention Optimization](https://mlsanity.com/paper/2503.07038/) (2025)
- [Automatic Bounding Box Annotation with Small Training Data Sets for Industrial Manufacturing](https://mlsanity.com/paper/2206.00280/) (2022)
- [One-Shot General Object Localization](https://mlsanity.com/paper/2211.13392/) (2022)
- [Sketch-based Video Object Localization](https://mlsanity.com/paper/2304.00450/) (2023)
- [GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection](https://mlsanity.com/paper/2312.16571/) (2023)
- [Accurate and Fast Pixel Retrieval with Spatial and Uncertainty Aware Hypergraph Diffusion](https://mlsanity.com/paper/2406.11242/) (2024)

## Abstract (arXiv)

In this paper, we consider the task of one-shot object detection, which consists in detecting objects defined by a single demonstration. Differently from the standard object detection, the classes of objects used for training and testing do not overlap. We build the one-stage system that performs localization and recognition jointly. We use dense correlation matching of learned local features to find correspondences, a feed-forward geometric transformation model to align features and bilinear resampling of the correlation tensor to compute the detection score of the aligned features. All the components are differentiable, which allows end-to-end training. Experimental evaluation on several challenging domains (retail products, 3D objects, buildings and logos) shows that our method can detect unseen classes (e.g., toothpaste when trained on groceries) and outperforms several baselines by a significant margin. Our code is available online: https://github.com/aosokin/os2d .