# TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation

arXiv: https://arxiv.org/abs/2409.05393 · HTML: https://mlsanity.com/paper/2409.05393/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 65 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 30 of 37)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Prior Guided Feature Enrichment Network for Few-Shot Segmentation](https://mlsanity.com/paper/2008.01449/) (2020)
- [FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation](https://mlsanity.com/paper/1612.02649/) (2016)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [Hypercorrelation Squeeze for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01538/) (2021)
- [Prototype Mixture Models for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2008.03898/) (2020)
- [FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation](https://mlsanity.com/paper/1907.12347/) (2019)
- [Domain Adaptation for Structured Output via Discriminative Patch Representations](https://mlsanity.com/paper/1901.05427/) (2019)
- [Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching](https://mlsanity.com/paper/2305.13310/) (2023)
- [Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?](https://mlsanity.com/paper/2012.06166/) (2020)
- [Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection](https://mlsanity.com/paper/2304.04709/) (2023)
- [ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1711.11556/) (2017)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything](https://mlsanity.com/paper/2304.06022/) (2023)
- [Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data](https://mlsanity.com/paper/2107.11978/) (2021)
- [A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1812.09953/) (2018)
- [Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining](https://mlsanity.com/paper/2401.08407/) (2024)
- [Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation](https://mlsanity.com/paper/1809.00903/) (2018)
- [Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2305.14335/) (2023)
- [DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2306.00499/) (2023)
- [APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2406.08372/) (2024)
- [Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning](https://mlsanity.com/paper/2308.16466/) (2023)

## Built on by (most cited first, 4 of 4)

- [Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation](https://mlsanity.com/paper/2502.03501/) (2025)
- [Boosting SAM for Cross-Domain Few-Shot Segmentation via Conditional Point Sparsification](https://mlsanity.com/paper/2602.05218/) (2026)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)
- [Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation](https://mlsanity.com/paper/2602.05217/) (2026)

## Abstract (arXiv)

While large visual models (LVM) demonstrated significant potential in image understanding, due to the application of large-scale pre-training, the Segment Anything Model (SAM) has also achieved great success in the field of image segmentation, supporting flexible interactive cues and strong learning capabilities. However, SAM's performance often falls short in cross-domain and few-shot applications. Previous work has performed poorly in transferring prior knowledge from base models to new applications. To tackle this issue, we propose a task-adaptive auto-visual prompt framework, a new paradigm for Cross-dominan Few-shot segmentation (CD-FSS). First, a Multi-level Feature Fusion (MFF) was used for integrated feature extraction as prior knowledge. Besides, we incorporate a Class Domain Task-Adaptive Auto-Prompt (CDTAP) module to enable class-domain agnostic feature extraction and generate high-quality, learnable visual prompts. This significant advancement uses a unique generative approach to prompts alongside a comprehensive model structure and specialized prototype computation. While ensuring that the prior knowledge of SAM is not discarded, the new branch disentangles category and domain information through prototypes, guiding it in adapting the CD-FSS. Comprehensive experiments across four cross-domain datasets demonstrate that our model outperforms the state-of-the-art CD-FSS approach, achieving an average accuracy improvement of 1.3% in the 1-shot setting and 11.76% in the 5-shot setting.