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Semantic Segmentation

Papers

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Showing 701–750 of 14763 papers

TitleStatusHype
Open-Set Domain Adaptation for Semantic SegmentationCode2
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion ModelsCode2
DAMamba: Vision State Space Model with Dynamic Adaptive ScanCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded ScenesCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
Customized Segment Anything Model for Medical Image SegmentationCode2
Dataset QuantizationCode2
Parameter-Inverted Image Pyramid NetworksCode2
PartSTAD: 2D-to-3D Part Segmentation Task AdaptationCode2
PEM: Prototype-based Efficient MaskFormer for Image SegmentationCode2
Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and VideosCode2
A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic SegmentationCode2
Cross-Image Relational Knowledge Distillation for Semantic SegmentationCode2
Cross Language Image Matching for Weakly Supervised Semantic SegmentationCode2
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic SegmentationCode2
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing ImagesCode2
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and BenchmarkCode2
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale AttentionCode2
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile ApplicationsCode2
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT ImagesCode2
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary SegmentationCode2
DAT++: Spatially Dynamic Vision Transformer with Deformable AttentionCode2
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and LocalizationCode2
Distribution-Free, Risk-Controlling Prediction SetsCode2
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentationCode2
CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything ModelCode2
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image FusionCode2
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic SegmentationCode2
LVOS: A Benchmark for Large-scale Long-term Video Object SegmentationCode2
ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic SegmentationCode2
RefMask3D: Language-Guided Transformer for 3D Referring SegmentationCode2
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene UnderstandingCode2
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic ApproximationCode2
ResT V2: Simpler, Faster and StrongerCode2
Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse WeatherCode2
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian SplattingCode2
Rethinking Interactive Image Segmentation with Low Latency High Quality and Diverse PromptsCode2
CellViT: Vision Transformers for Precise Cell Segmentation and ClassificationCode2
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything ModelCode2
COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional FeaturesCode1
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image SegmentationCode1
CP2: Copy-Paste Contrastive Pretraining for Semantic SegmentationCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
COSNet: A Novel Semantic Segmentation Network using Enhanced Boundaries in Cluttered ScenesCode1
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image SegmentationCode1
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic SegmentationCode1
Co-segmentation Inspired Attention Module for Video-based Computer Vision TasksCode1
Co-Scale Conv-Attentional Image TransformersCode1
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4InternImage-HValidation mIoU62.9—Unverified
5M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified