# Semantic-aligned Fusion Transformer for One-shot Object Detection

arXiv: https://arxiv.org/abs/2203.09093 · HTML: https://mlsanity.com/paper/2203.09093/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 66 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 30 of 55)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [Few-shot Object Detection via Feature Reweighting](https://mlsanity.com/paper/1812.01866/) (2018)

## Built on by (most cited first, 13 of 13)

- [Playing to Vision Foundation Model's Strengths in Stereo Matching](https://mlsanity.com/paper/2404.06261/) (2024)
- [QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition](https://mlsanity.com/paper/2310.00132/) (2023)
- [Detect Everything with Few Examples](https://mlsanity.com/paper/2309.12969/) (2023)
- [On the Importance of Spatial Relations for Few-shot Action Recognition](https://mlsanity.com/paper/2308.07119/) (2023)
- [Panoramic Video Salient Object Detection with Ambisonic Audio Guidance](https://mlsanity.com/paper/2211.14419/) (2022)
- [LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation](https://mlsanity.com/paper/2211.16290/) (2022)
- [PRVQL: Progressive Knowledge-guided Refinement for Robust Egocentric Visual Query Localization](https://mlsanity.com/paper/2502.07707/) (2025)
- [Masked autoencoders are effective solution to transformer data-hungry](https://mlsanity.com/paper/2212.05677/) (2022)
- [ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detection](https://mlsanity.com/paper/2508.01253/) (2025)
- [Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection](https://mlsanity.com/paper/2606.23069/) (2026)
- [Adaptive Base-class Suppression and Prior Guidance Network for One-Shot Object Detection](https://mlsanity.com/paper/2303.14240/) (2023)
- [AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT](https://mlsanity.com/paper/2309.08134/) (2023)
- [Indirect Attention: Turning Context Misalignment into a Feature](https://mlsanity.com/paper/2509.26015/) (2025)

## Abstract (arXiv)

One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowledge. Yet, their performances are often unsatisfactory. In this paper, we attribute this to inappropriate correlation methods that misalign query-support semantics by overlooking spatial structures and scale variances. Upon analysis, we leverage the attention mechanism and propose a simple but effective architecture named Semantic-aligned Fusion Transformer (SaFT) to resolve these issues. Specifically, we equip SaFT with a vertical fusion module (VFM) for cross-scale semantic enhancement and a horizontal fusion module (HFM) for cross-sample feature fusion. Together, they broaden the vision for each feature point from the support to a whole augmented feature pyramid from the query, facilitating semantic-aligned associations. Extensive experiments on multiple benchmarks demonstrate the superiority of our framework. Without fine-tuning on novel classes, it brings significant performance gains to one-stage baselines, lifting state-of-the-art results to a higher level.