Semantic Segmentation

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21 papers in the last four weeks, up 24% on the four weeks before. 0.2% of all new papers.

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Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems
May 14, 2026Emilio Mastriani, Alessandro Costa, Federico Incardona +2Interpretable ML Predictive Maintenance
INAF, Osservatorio Astrofisico di Catania, Via S. Sofia 78, Catania, 95123, Italy
Towards Real-Time Autonomous Navigation: Transformer-Based Catheter Tip Tracking in Fluoroscopy
May 14, 2026Harry Robertshaw, Yanghe Hao, Weiyuan Deng +6Medical Image Segmentation Semantic Segmentation
Surgical & Interventional Engineering School of Biomedical Engineering & Imaging Sciences2026 Kings College London London · School of Engineering & Materials Science Queen Mary London14 London
Weakly Supervised Segmentation as Semantic-Based Regularization
May 13, 2026Stefano Colamonaco, Andrei-Bogdan Florea, Jaron MaeneImage Segmentation Weakly Supervised Image Segmentation
KU Leuven · Apixa
Towards Unified Surgical Scene Understanding:Bridging Reasoning and Grounding via MLLMs
May 13, 2026Jincai Huang, Shihao Zou, Yuchen Guo +5Surgical Video Understanding Vision-Language Grounding
Southern University of Science and Technology · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Northwestern University +4
From Web to Pixels: Bringing Agentic Search into Visual Perception
May 12, 2026Bokang Yang, Xinyi Sun, Kaituo Feng +3Visual Question Answering Vision-Language Grounding
1Shenzhen Loop Area Institute · 2Wuhan University · 3CUHK MMLab
AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection
May 12, 2026Joaquín Figueira, Rob Van Gastel, Giacomo D'Amicantonio +4Self-Supervised Pre-Training Semiconductor Manufacturing
AIMS Lab, Eindhoven University of Technology · CoC, ASMPT · iCIS, Radboud University
What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization
May 12, 2026Ryota Yoshihashi, Masahiro Kada, Satoshi Ikehata +2Visual Attention Vision Transformer
Institute of Science Tokyo · 2DENSO IT Laboratory · 3National Institute of Informatics
Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation
May 12, 2026Joel Valdivia Ortega, Tingying Peng, Marion JasninHybrid CNN-Transformer Architectures Vision Foundation Model Adaptation
Helmholtz AI, Helmoltz Munich, Neuherberg, Germany · Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany · School of Computation, Information and Technology, TUM, Garching, Germany +1
REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View
May 12, 2026Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman +1Camera-Radar Fusion Bird's-Eye-View Representation
Elektrobit Automotive GmbH · Transilvania University of Brasov · Eindhoven University of Technology
Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation
May 11, 2026Federico Pizzolato, Francesco Pasti, Nicola BellottoRobot Navigation Semantic Segmentation
Dept of Information Engineering, University of Padua, Italy
DA-SegFormer: Damage-Aware Semantic Segmentation for Fine-Grained Disaster Assessment
May 11, 2026Kevin Zhu, William Tang, Raphael Hay Tene +3Class-Imbalanced Learning Image Segmentation
Bina Labs, Lehigh University
From Pixels to Concepts: Do Segmentation Models Understand What They Segment?
May 10, 2026Shuang Liang, Zeqing Wang, Yuxian Li +2VLM Evaluation Image Segmentation
Department of Electrical and Computer Engineering, The University of Hong Kong · CASIC · School of Computer Science and Engineering, Sun Yat-sen University +1
UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition
May 8, 2026Shuai Zhang, Zhecheng Shi, Zhuxiao Li +4Disentangled Representation Learning Multimodal Robustness
1HKUST(GZ) · 2HKUST
Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
May 7, 2026Aditya Parikh, Stella Frank, Sneha Das +1Image Segmentation Algorithmic Fairness
Department of Applied Mathematics and Computer Science Technical University of Denmark
ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision
May 7, 2026Ke Zhang, Bomin Wang, Hangqi Zhou +1Image Segmentation Weakly Supervised Image Segmentation
School of Data Science, Fudan University, Shanghai, 200433, China · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching
May 6, 2026Andranik Sargsyan, Shant NavasardyanImage Segmentation Text-Guided Image Segmentation
Picsart AI Research (PAIR)
Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning
May 6, 2026Yida Lin, Bing Xue, Mengjie Zhang +2Aerial Robotics Stereo Matching
Centre of Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand · Computer Vision Research Lab & Department of Computer Science and Software Engineering, Canterbury University, PO Box 8041, Christchurch, New Zealand
DiCLIP: Diffusion Model Enhances CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
May 6, 2026Zhiwei Yang, Pengfei Song, Yucong Meng +3Vision-Language Models Weakly Supervised Image Segmentation
Academy for Engineering and Technology, Fudan University, Shanghai 200433, China · Shandong Computer Science Center, China · Digital Medical Research Center, School of Basic Medical Science, Fudan University, and also with Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, China +1
From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation
May 6, 2026Zishen Qu, Xuesong Li, Haijian Gu +5Image Segmentation Text-Guided Image Segmentation
Zhejiang University · bytedance · Southeast University
RD-ViT: Recurrent-Depth Vision Transformer for Semantic Segmentation with Reduced Data Dependence Extending the Recurrent-Depth Transformer Architecture to Dense Prediction
May 5, 2026Renjie HeMedical Image Analysis Recurrent Transformers
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
May 5, 2026Shishir Muralidhara, Didier Stricker, René SchusterDomain-Incremental Learning Incremental Learning
German Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany · University of Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Str. 47, 67663 Kaiserslautern, Germany
Dual-Foundation Models for Unsupervised Domain Adaptation
May 5, 2026Yerin Cheon, Aruna Balasubramanian, Francois RameauSynthetic-to-Real Domain Adaptation Unsupervised Domain Adaptation
Stony Brook University, Stony Brook, NY, USA · SUNY Korea, The State University of New York, Korea
FoR-Net: Focus-on-Regions Network for Semantic Segmentation
May 4, 2026Sheng-Wei Chan, Hsin-Jui Pan, Jen-Shiun ChiangSemantic Segmentation
Department of Electrical and Computer Engineering, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist., New Taipei City, 251301, Taiwan
UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation
May 4, 2026Conghui Li, Huanyu He, Xin Wang +2Segmentation Uncertainty Estimation Crack Segmentation
School of Information Technology, Monash University, Selangor, Malaysia · Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China · School of Engineering, Monash University, Selangor, Malaysia
SpectraDINO: Bridging the Spectral Gap in Vision Foundation Models via Lightweight Adapters
May 4, 2026Yagiz Nalcakan, Hyeongjin Ju, Incheol Park +3Cross-Modal Knowledge Distillation Multispectral Object Detection
Yonsei University, Seoul, Republic of Korea
A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation
Apr 30, 2026Yang Zhou, Chaoyong Zhang, Ruoyi Hao +3Medical Image Segmentation Semantic Segmentation
School of Mechanical Science & Engineering Huazhong University of Science and Technology, Wuhan 430074, China · Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China · Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China +5
Graph-based Semantic Calibration Network for Unaligned UAV RGBT Image Semantic Segmentation and A Large-scale Benchmark
Apr 29, 2026Fangqiang Fan, Zhicheng Zhao, Xiaoliang Ma +2Cross-Modal Alignment Graph Reasoning
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei 230601, China · School of Computer Science and Technology, Anhui University, Hefei 230601, China · GEOVIS Earth Technology Co., Ltd., Hefei 230088, China +1
The Surprising Effectiveness of Canonical Knowledge Distillation for Semantic Segmentation
Apr 28, 2026Muhammad Ali, Kevin Alexander Laube, Madan Ravi Ganesh +3Image Segmentation Feature-Based Knowledge Distillation
University of Freiburg · Bosch Center for Artificial Intelligence · Aleph Alpha Research +1
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
Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions
Apr 27, 2026Yangping Li, Thomas Pinetz, Michael Hölzel +2Computational Pathology Image Segmentation
Institute of Pathology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127 Bonn, Germany · Institute for Applied Mathematics, University of Bonn, Endenicher Allee 60, 53115 Bonn, Germany · Institute of Experimental Oncology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127 Bonn, Germany

May 14, 2026, cs.AIJ/K move · Enter open · S save

Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems

Emilio Mastriani, Alessandro Costa, Federico Incardona, Kevin Munari+1

INAF, Osservatorio Astrofisico di Catania, Via S. Sofia 78, Catania, 95123, Italy

Interpretable ML Predictive Maintenance Semantic Segmentation

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Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-relevant information and reduce model interpretability. This work proposes a semantic feature segmentation framework that decomposes the monitored feature space into a canonical component,expected to retain the dominant predictive information, and a residual component containing structurally peripheral signals. The segmentation is defined through domain informed criteria and sets up monitoring variables into functional groups reflecting operational mechanisms such as throughput,latency,pressure,network activity,and structural state. To evaluate the effectiveness of this decomposition, we adopt a predictive perspective in which expected predictive risk is used as an operational proxy for task-relevant information. Experimental results obtained through time-aware cross-validation show that the canonical space consistently achieves lower predictive risk than the residual space across multiple temporal configurations, indicating that the semantic segmentation concentrates the most relevant information for fault anticipation. In addition, the canonical segments exhibit significantly stronger intra-segment coherence than inter-segment dependence, and this structural organization remains stable after redundancy reduction. When compared with the full feature space and with a Principal Component Analysis (PCA) representation, the canonical space carries out comparable predictive performance and furthermore preserves the semantic meaning of the original variables. These findings suggest that semantic feature segmentation provides an interpretable and information-preserving decomposition of monitoring signals, enabling competitive predictive performance without sacrificing the operational interpretability required in predictive maintenance applications.

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