Open-Vocabulary Semantic Segmentation

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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
Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation
Jun 6, 2026Yang Sun, Tao Wang, Anastasia Ioannou +1VLM Adaptation Open-Vocabulary Semantic Segmentation
College of Computer and Data Science, Fuzhou University, Fuzhou, China. · School of Computer and Big Data, Minjiang University, Fuzhou, China. · Department of Computer Science and Engineering, European University Cyprus, Nicosia, Cyprus.
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.
WOW-Seg: A Word-free Open World Segmentation Model
May 16, 2026Danyang Li, Tianhao Wu, Bin Li +5Image Segmentation Open-Set Recognition
2VCIP, CS, Nankai University · 1NKIARI, Shenzhen Futian · 3AAIS, Nankai University +2
Decomposed Vision-Language Alignment for Fine-Grained Open-Vocabulary Segmentation
May 15, 2026Chenhao Wang, Yingrui Ji, Yu Meng +1Image Segmentation Open-Vocabulary Semantic Segmentation
Aerospace Information Research Institute, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Zhejiang University
VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference
May 12, 2026Hao Zhu, Shuo Jin, Wenbin Liao +4Image Segmentation Vision-Language Models
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China · XJTLU +1
Semantic Alignment in Hyperbolic Space for Open-Vocabulary Semantic Segmentation
May 9, 2026Hoang M. Truong, Hai Nguyen-Truong, Dang HuynhVLM Adaptation Hierarchical Representation Learning
Fulbright University Vietnam
DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery
May 4, 2026Ryan Faulkenberry, Saurabh PrasadImage Segmentation Remote Sensing Image Segmentation
University of Houston
Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
Apr 29, 2026Guanchun Wang, Chenxiao Wu, Xiangrong Zhang +4Remote Sensing Image Segmentation Test-Time Adaptation
School of Artificial Intelligence, Xidian University · MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University
DouC: Dual-Branch CLIP for Training-Free Open-Vocabulary Segmentation
Apr 27, 2026Mohamad Zamini, Diksha ShuklaImage Segmentation Open-Vocabulary Semantic Segmentation
Department of Electrical Engineering and Computer Science, University of Wyoming, Laramie, WY, USA.
X2SAM: Any Segmentation in Images and Videos
Apr 27, 2026Hao Wang, Limeng Qiao, Chi Zhang +4Image Segmentation Video Object Segmentation
Sun Yat-Sen University · 2 Peng Cheng Laboratory · 3 Meituan Inc
Diffusion Model as a Generalist Segmentation Learner
Apr 27, 2026Haoxiao Wang, Antao Xiang, Haiyang Sun +8Image Segmentation Text-Guided Image Segmentation
Zhejiang University · South China University of Technology · Nanjing University +1
Open-Vocabulary Semantic Segmentation Network Integrating Object-Level Label and Scene-Level Semantic Features for Multimodal Remote Sensing Images
Apr 27, 2026Jinkun Dai, Yuanxin Ye, Peng Tang +4Remote Sensing Image Segmentation Text-Guided Image Segmentation
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China · State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China · School of Artificial Intelligence, Wuhan University, Wuhan 430072, China +1
Semantic-Fast-SAM: Efficient Semantic Segmenter
Apr 22, 2026Byunghyun KimImage Segmentation Vision Foundation Model Adaptation
Kyungpook National University, Korea
CoCo-SAM3: Harnessing Concept Conflict in Open-Vocabulary Semantic Segmentation
Apr 21, 2026Yanhui Chen, Baoyao Yang, Siqi Liu +1Promptable Image Segmentation Open-Vocabulary Semantic Segmentation
School of Computers, Guangdong University of Technology · Shenzhen Research Institute of Big Data · Peking University
Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge
Apr 21, 2026Zihao Ye, Yung-Hsiang Lu, Xiao Hu +14Edge Inference Monocular Depth Estimation
Purdue University, West Lafayette, IN, USA · Qualcomm, USA · Lehigh University, Bethlehem, PA, USA +3
T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability
Apr 20, 2026Savya Khosla, Sethuraman T, Aryan Chadha +2VLM Adaptation Open-Vocabulary Semantic Segmentation
University of Illinois Urbana-Champaign
SENSE: Stereo OpEN Vocabulary SEmantic Segmentation
Apr 17, 2026Thomas Campagnolo, Ezio Malis, Philippe Martinet +1Open-Vocabulary Semantic Segmentation
ACENTAURI · Centre Inria d’Universite Cote d’Azur, France · NXP Semiconductors, France
Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Apr 17, 2026Bingyu Li, Tao Huo, Haocheng Dong +4Image Segmentation Remote Sensing Image Segmentation
Department of Electronic Engineering and Information Science, University of Science and Technology of China, China · Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China · School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, China
JOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas
Mar 6, 2026Sandeep Inuganti, Hideaki Kanayama, Kanta Shimizu +43D Point Cloud Segmentation Open-Vocabulary 3D Segmentation
German Research Center for Artificial Intelligence, DFKI, Germany · RPTU Kaiserslautern, Germany · Ricoh Company, Ltd. Japan +1
SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation
Aug 8, 2025Weichen Zhang, Kebin Liu, Fan Dang +3Contrastive Learning Weakly Supervised Image Segmentation
Tsinghua University, 100084, Beijing, China · Beijing Jiaotong University, 100044, Beijing, China
LlamaSeg: Image Segmentation via Autoregressive Mask Generation
May 26, 2025Jiru Deng, Tengjin Weng, Tianyu Yang +3Image Segmentation Text-Guided Image Segmentation
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Tsinghua University · Shenzhen University +2
Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks
May 6, 2025Haonan Wang, Jiaji Mao, Lehan Wang +11Image Segmentation Medical Imaging Foundation Models
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR · Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR

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Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration

Silas Kwabla Gah, Ebenezer Owusu

Department of Computer Science University of Ghana

Few-Shot Semantic Segmentation Open-Vocabulary Semantic Segmentation Semantic Segmentation

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Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adaptation to incorporate novel classes from limited support examples. Recent foundation models, however, already exhibit strong open-vocabulary recognition and segmentation capabilities, raising a different question: can GFSS be solved through inference-time coordination of frozen semantic priors rather than parameter adaptation? We answer this question with Open-V, a training-free GFSS framework that combines Segment Anything (SAM3) Promptable Concept Segmentation (PCS) with a K-shot CLIP support centroid through calibrated per-pixel semantic arbitration. OpenV introduces no trainable components and supports arbitrary semantic categories at inference time. Beyond segmentation performance, our study contributes three broader findings. First, we show that support information can be incorporated through inference-time semantic grounding, and that its contribution increases as foundation-model text priors weaken on label-disjoint vocabularies. Second, we identify a reproducibility confound in foundationmodel segmentation, demonstrating that preprocessing and evaluation-space mismatches can silently distort reported performance. Finally, we validate Open-V across PASCAL5i, COCO-20i, and ADE-OW, showing that training-free coordination of foundation-model priors generalizes across both conventional GFSS and open-vocabulary evaluation settings. On PASCAL-5i (1-shot), Open-V attains base/novel/harmonic mIoU of 78.4/77.5/77.9, without GFSS-specific training surpassing the strongest trained baseline by +17.7 HM.

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