# Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

arXiv: https://arxiv.org/abs/2402.03094 · HTML: https://mlsanity.com/paper/2402.03094/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 61 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 30 of 51)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [RegionCLIP: Region-based Language-Image Pretraining](https://mlsanity.com/paper/2112.09106/) (2021)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Open-Vocabulary Object Detection Using Captions](https://mlsanity.com/paper/2011.10678/) (2020)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (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)
- [Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation](https://mlsanity.com/paper/1803.11365/) (2018)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation](https://mlsanity.com/paper/2001.08735/) (2020)
- [Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning](https://mlsanity.com/paper/1909.13032/) (2019)
- [FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding](https://mlsanity.com/paper/2103.05950/) (2021)
- [Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference](https://mlsanity.com/paper/2204.07305/) (2022)
- [DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection](https://mlsanity.com/paper/2108.09017/) (2021)
- [Exploiting Unlabeled Data with Vision and Language Models for Object Detection](https://mlsanity.com/paper/2207.08954/) (2022)
- [Generalized Few-Shot Object Detection without Forgetting](https://mlsanity.com/paper/2105.09491/) (2021)
- [Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation](https://mlsanity.com/paper/2004.06042/) (2020)

## Built on by (most cited first, 30 of 43)

- [Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community](https://mlsanity.com/paper/2408.09110/) (2024)
- [Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2504.04517/) (2025)
- [EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering](https://mlsanity.com/paper/2508.10729/) (2025)
- [EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark](https://mlsanity.com/paper/2510.06218/) (2025)
- [Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2506.05872/) (2025)
- [EarthSynth: Generating Informative Earth Observation with Diffusion Models](https://mlsanity.com/paper/2505.12108/) (2025)
- [3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection](https://mlsanity.com/paper/2507.23567/) (2025)
- [Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves](https://mlsanity.com/paper/2412.11509/) (2024)
- [No time to train! Training-Free Reference-Based Instance Segmentation](https://mlsanity.com/paper/2507.02798/) (2025)
- [Connecting Giants: Synergistic Knowledge Transfer of Large Multimodal Models for Few-Shot Learning](https://mlsanity.com/paper/2510.11115/) (2025)
- [INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning](https://mlsanity.com/paper/2412.03565/) (2024)
- [InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition](https://mlsanity.com/paper/2505.15818/) (2025)
- [SpecDETR: A transformer-based hyperspectral point object detection network](https://mlsanity.com/paper/2405.10148/) (2024)
- [Teaching VLMs to Localize Specific Objects from In-context Examples](https://mlsanity.com/paper/2411.13317/) (2024)
- [Cross-domain Multi-modal Few-shot Object Detection via Rich Text](https://mlsanity.com/paper/2403.16188/) (2024)
- [AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks](https://mlsanity.com/paper/2411.16749/) (2024)
- [CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion](https://mlsanity.com/paper/2505.00938/) (2025)
- [Zero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptation](https://mlsanity.com/paper/2508.03300/) (2025)
- [Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2602.18811/) (2026)
- [Beyond Few-shot Object Detection: A Detailed Survey](https://mlsanity.com/paper/2408.14249/) (2024)
- [Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images](https://mlsanity.com/paper/2504.06330/) (2025)
- [Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models](https://mlsanity.com/paper/2601.08476/) (2026)
- [FSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusion](https://mlsanity.com/paper/2602.03137/) (2026)
- [Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2603.18541/) (2026)
- [A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps](https://mlsanity.com/paper/2603.28182/) (2026)
- [OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection](https://mlsanity.com/paper/2503.06442/) (2025)
- [VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection](https://mlsanity.com/paper/2508.11167/) (2025)
- [ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition](https://mlsanity.com/paper/2510.16822/) (2025)
- [Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts](https://mlsanity.com/paper/2510.19487/) (2025)
- [Practical Insights into Semi-Supervised Object Detection Approaches](https://mlsanity.com/paper/2601.13380/) (2026)

## Abstract (arXiv)

This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, their generalization to CD-FSOD remains unclear: 1) can such open-set detection methods easily generalize to CD-FSOD? 2) If not, how can models be enhanced when facing huge domain gaps? To answer the first question, we employ measures including style, inter-class variance (ICV), and indefinable boundaries (IB) to understand the domain gap. Based on these measures, we establish a new benchmark named CD-FSOD to evaluate object detection methods, revealing that most of the current approaches fail to generalize across domains. Technically, we observe that the performance decline is associated with our proposed measures: style, ICV, and IB. Consequently, we propose several novel modules to address these issues. First, the learnable instance features align initial fixed instances with target categories, enhancing feature distinctiveness. Second, the instance reweighting module assigns higher importance to high-quality instances with slight IB. Third, the domain prompter encourages features resilient to different styles by synthesizing imaginary domains without altering semantic contents. These techniques collectively contribute to the development of the Cross-Domain Vision Transformer for CD-FSOD (CD-ViTO), significantly improving upon the base DE-ViT. Experimental results validate the efficacy of our model.