Fine-Grained Semantic Segmentation

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10 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

CoordFormer: Give Me Any Coordinates and I Will Give You Labels
Sep 8, 2026Iacopo Curti, Pierluigi Zama Ramirez, Alioscia Petrelli +1Efficient Semantic Segmentation Vision Transformer
CVLab, University of Bologna · Ca’ Foscari University of Venice · SINA, company
Toward Robust LiDAR Semantic Segmentation for Real-World Deployment: Evaluation under Coarse Labels, Adverse Conditions, and Domain Shifts
Sep 2, 2026Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich +2Fine-Grained Semantic Segmentation Semantic Segmentation
Mines Paris, PSL University, Centre for Robotics (CAOR), 75006 Paris, France · Paris-Saclay University, CEA, List, F-91120, Palaiseau, France
Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding
Jun 22, 2026Boyan Wang, Yongxi Huang, Wenjing Li +4Vision Foundation Model Adaptation Deep Ensembles
LION Lab, Hefei University of Technology, Hefei, China. · University of Macau, Macau, China.
Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Exploring Query-Based Segmentation and Increased Spatial Context for Outdoor Scene Understanding
Jun 19, 2026David Pascual-Hernández, Roberto Calvo-Palomino, Inmaculada Mora-Jiménez +1Image Segmentation Fine-Grained Semantic Segmentation
1Authors are with Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Fuenlabrada, 28943, Spain.
SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation
Jun 18, 2026Xuesong WangVision Foundation Models Self-Distillation
Wayne State University, Detroit, MI, USA
Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Leveraging DINOv3 for Robust Outdoor Scene Understanding in Field Robotics
Jun 17, 2026Jaeil Park, Hyobin Choi, Sangjin Lee +2Robotics Robotic Perception
Multimodal Intelligence and Perception Laboratory (MIP), Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea. · Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain
Jun 14, 2026Jyothiraditya Lingam, Nikhileswara Rao Sulake, Sai Manikanta Eswar MacharaInstance Segmentation Fine-Grained Semantic Segmentation
Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies, Nuzvid, India
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
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
Excite, Attend and Segment (EASe): Domain-Agnostic Fine-Grained Mask Discovery with Feature Calibration and Self-Supervised Upsampling
Mar 31, 2026Deepank Singh, Anurag Nihal, Vedhus HoskereFine-Grained Semantic Segmentation Image Segmentation
University of Houston, Houston, TX, USA

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CoordFormer: Give Me Any Coordinates and I Will Give You Labels

Iacopo Curti, Pierluigi Zama Ramirez, Alioscia Petrelli, Luigi Di Stefano

CVLab, University of Bologna · Ca’ Foscari University of Venice · SINA, company

Efficient Semantic Segmentation Vision Transformer Semantic Segmentation Fine-Grained Semantic Segmentation

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Semantic segmentation on very-high-resolution images remains challenging due to the high computational cost and the difficulty of capturing fine-grained details. We propose CoordFormer, a novel coordinate-based architecture for semantic segmentation that predicts labels at arbitrary spatial locations through a Coordinate Decoder equipped with a Localized Cross-Attention mechanism. The decoder combines coordinate embeddings with high-resolution local patch features and interacts with global tokens extracted from a downsampled image processed by a ViT foundation encoder, enabling rich semantic context while preserving pixel-level precision. This design enables flexible inference at arbitrary resolutions while keeping memory low on very-high-resolution inputs, and supports an efficient semantic-edge-focused strategy that concentrates computation along boundaries, maintaining fine-grained accuracy while reducing latency and computational cost. CoordFormer achieves state-of-the-art performance on MaSS13K and outperforms comparably sized and higher-parameter methods on DIS5K and KPIs, demonstrating its effectiveness for high-quality, very-high-resolution semantic segmentation.