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Papers for “semantic segmentation”

Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.

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TitleAuthorsYearSource
MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic SegmentationChenfei Liao, Xu Zheng, Yuanhuiyi Lyu2025arxiv 2503.06700
Looking Beyond the Window: Global-Local Aligned CLIP for Training-free Open-Vocabulary Semantic SegmentationByeongCheol Lee, Hyun Seok Seong, Sangeek Hyun2026arxiv 2603.23030
CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited LabelsPing Guo, Chengzhou Li, Guanchen Meng2026arxiv 2603.21071
Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning methodXin Zhang, Bingfang Wu, Liang Zhu2018arxiv 1804.00498
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion NetworksSerdar Erisen2024arxiv 2401.15741
Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural EigenspaceZhiyi Pan, Peng Jiang, Changhe Tu2020arxiv 2011.05621
Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic DataMarcus G Müller, Wout Boerdijk, Maximilian Durner2026arxiv 2605.27644
Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic SegmentationLucas Görnhardt, Timo Bartels, Niklas Schwarz2026arxiv 2606.09536
Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA WorkflowsJulien Posso, Hugo Kieffer, Nicolas Menga2025arxiv 2503.08700
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic SegmentationPeng-Tao Jiang, Yuqi Yang2023arxiv 2305.01275
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training ApproachMin Yan, Qianxiong Ning, Qian Wang2023arxiv 2306.03508
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic SegmentationLokesh Veeramacheneni, Matias Valdenegro-Toro2022arxiv 2211.06241
Union-set Multi-source Model Adaptation for Semantic SegmentationZongyao Li, Ren Togo, Takahiro Ogawa2022arxiv 2212.02785
AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic SegmentationMd. Al-Masrur Khan, Durgakant Pushp, Lantao Liu2025arxiv 2507.17957
Context-Guided Spatial Feature Reconstruction for Efficient Semantic SegmentationZhenliang Ni, Xinghao Chen, Yingjie Zhai2024arxiv 2405.06228
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic SegmentationYu Qiao, Jincheng Zhu, Chengjiang Long2021arxiv 2112.05975
Learning Semantic Segmentation from Multiple Datasets with Label ShiftsDongwan Kim, Yi-Hsuan Tsai, Yumin Suh2022arxiv 2202.14030
Reliability in Semantic Segmentation: Can We Use Synthetic Data?Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen2023arxiv 2312.09231
DUDA: Distilled Unsupervised Domain Adaptation for Lightweight Semantic SegmentationBeomseok Kang, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi2025arxiv 2504.09814
Spectral-Aware Global Fusion for RGB-Thermal Semantic SegmentationCe Zhang, Zifu Wan, Simon Stepputtis2025arxiv 2505.15491