Few-Shot Semantic Segmentation

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Exploiting Target Knowledge from MLLMs for Robust Few-Shot Segmentation
Sep 24, 2026Yijun Hu, Heng Fan, Libo ZhangFew-Shot Semantic Segmentation
UCAS
Beyond Argmax: A Mechanistic Study of Semantic Retention in Frozen Foundation-Model Composition for Generalized Few-Shot 3D Segmentation
Sep 14, 2026Silas Kwabla Gah, Ebenezer Owusu3D Foundation Models Ensemble Learning
Department of Computer Science University of Ghana, Legon
FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation
Sep 3, 2026Ming Hu, Jianfu Yin, Mingyu Dou +5Image Segmentation Few-Shot Semantic Segmentation
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Xi’an Jiaotong University +1
Exemplar: Classical Priors Complement Frozen Features for Few-Shot Microscopy Segmentation at Native Resolution
Sep 2, 2026Michal Průšek, Adam Novozámský, Filip ŠroubekImage Segmentation Few-Shot Semantic Segmentation
The Czech Academy of Sciences, Institute of Information Theory and Automation, Czechia
Training-Free Entity-Level Few-Shot Segmentation of Remote Sensing Images with Advection Refinement
Jul 31, 2026Xueting Bai, Huan NiRemote Sensing Image Segmentation Vision Foundation Model Adaptation
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion
Jul 28, 2026Junhyuk Heo, Junghwan ParkRemote Sensing Image Segmentation Few-Shot Semantic Segmentation
TelePIX, Seoul, Republic of Korea
SADe: Sparse-Atom Support Decontamination for Few-Shot Segmentation with Weak Support Annotations
Jul 27, 2026Hang Xing, Guangjun Liu, Yan Xia +1Weakly Supervised Image Segmentation Few-Shot Semantic Segmentation
Wuxi Key Laboratory of Photovoltaic Equipment Advanced Manufacturing Technology, Wuxi, China · Wuxi Autowell Technology Co., Ltd., Wuxi, China · Wuxi Autowell Coshin Semiconductor Technology Co., Ltd., Wuxi, China
REBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation
Jul 10, 2026Mantha Sai Gopal, Jaison Saji Chacko, Harsh Nandwana +3Image Segmentation Promptable Image Segmentation
CamCom Technologies Private Limited*
FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics
Jun 30, 2026Junghwan ParkRemote Sensing Image Segmentation Few-Shot Semantic Segmentation
TelePIX
Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching
Jun 23, 2026Sujun Sun, Mingwu Ren, Haofeng ZhangFew-Shot Learning Cross-Domain Transfer Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, China · State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery, China
Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation
Jun 23, 2026Sujun Sun, Mingwu Ren, Haofeng ZhangPrototype-Based Learning Cross-Domain Generalization
School of Computer Science and Engineering, Nanjing University of Science and Technology, China · State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery, China
Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration
Jun 8, 2026Silas Kwabla Gah, Ebenezer OwusuFew-Shot Semantic Segmentation Open-Vocabulary Semantic Segmentation
Department of Computer Science University of Ghana
Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline
May 21, 2026Sebastian Cavada, Francesco Pelosin, Lapo FaggiPrototype-Based Classification Image Segmentation
Covision Lab, Bressanone, South Tyrol, Italy
Continual Segmentation under Joint Nonstationarity
May 19, 2026Prashant Pandey, Himanshu Kumar, Devineni Sri Venkatraya Chowdary +1Class-Incremental Learning Continual Learning
Bharti School of Telecommunications Technology & Management, Indian Institute of Technology, Delhi, India · Yardi School of Artificial Intelligence, Indian Institute of Technology, Delhi, India · Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India
PrAda: Few-Shot Visual Adaptation for Text-Prompted Segmentation
May 19, 2026Gabriele Rosi, Fabio Cermelli, Carlo Masone +1VLM Adaptation Text-Guided Image Segmentation
Politecnico di Torino · Focoos AI
Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation
May 19, 2026Junyuan Ma, Xunzhi Xiang, Wenbin Li +2Vision Foundation Model Adaptation Few-Shot Learning
1Nanjing University
SegRAG: Retrieval Augmented Spatial Prompting for Open Vocabulary Semantic Segmentation
May 17, 2026Abderrahmene Boudiaf, Irfan Hussain, Sajid JavedImage Segmentation Promptable Image Segmentation
Khalifa University, United Arab Emirates.
Cryo-Bench: Benchmarking Foundation Models for Cryosphere Mapping
Mar 2, 2026Saurabh Kaushik, Lalit Maurya, Beth Tellman +4Remote Sensing Image Segmentation Geospatial Foundation Models
Center for Sustainability and the Global Environment (SAGE), University of Wisconsin–Madison USA · Portsmouth AI and Data Science Centre (PAIDS), School of Computing, University of Portsmouth Portsmouth, UK · ESA, ESRIN, 𝜑-lab, Frascati Italy
Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation
Sep 16, 2025Qianguang Zhao, Dongli Wang, Yan Zhou +23D Point Cloud Segmentation Prototype Learning
School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China · School of Automation and Electronics Information, Xiangtan University, Xiangtan 411105, China · School of Automation, Shanghai Jiao Tong University, Shanghai 200232, China
Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Aug 22, 2025Jiaqi Ma, Guo-Sen Xie, Fang Zhao +1Weakly Supervised Image Segmentation Weakly Supervised Semantic Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China

Sep 24, 2026, cs.CVJ/K move · Enter open · S save

Exploiting Target Knowledge from MLLMs for Robust Few-Shot Segmentation

Yijun Hu, Heng Fan, Libo Zhang

UCAS

Few-Shot Semantic Segmentation

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Few-shot segmentation (FSS) aims to segment unseen object categories with a few (e.g., one or five) labeled examples, enabling efficient adaptation to novel classes. Conventional models typically rely on appearance-based visual matching between support and query images for segmentation. While straightforward, these methods often struggle to handle significant appearance discrepancies and occlusions in the query image due to insufficient target knowledge. To mitigate this, we introduce a novel framework that mines target knowledge using the strong reasoning capacity of Multimodal Large Language Models (MLLMs) and employs it to enhance FSS. Specifically, building on SAM 2, our method, named MK-FSS, exploits two forms of complementary knowledge derived from a query image by an MLLM for FSS, including spatial knowledge, which provides a spatial prior indicating the potential target location, and semantic knowledge, which describes the target using text. The spatial knowledge is first encoded into a memory representation, and then resulting memory is integrated with the support-guided memory feature from query image through a carefully designed dual-memory debate-fusion (DMDF) module, yielding a more robust target memory feature. In parallel, the semantic knowledge is encoded into the textual feature, which is fused with multi-scale query features via a progressive cross-modal prompt generator (PCPG), producing a target-aware multimodal prompt for segmentation. Working together, the dual-memory feature and the multimodal prompt provide a comprehensive representation of the target, enabling more robust segmentation. In our extensive experiments, MK-FSS shows promising results and largely surpasses existing methods. Code will be released.

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