SOTAVerified

Semantic Segmentation

Papers

Showing 951975 of 14763 papers

TitleStatusHype
TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of SightCode1
EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge DevicesCode1
Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image SegmentationCode1
SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote SensingCode1
LESS: Label-Efficient and Single-Stage Referring 3D SegmentationCode1
EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment AnythingCode1
Agent Skill Acquisition for Large Language Models via CycleQDCode1
VividMed: Vision Language Model with Versatile Visual Grounding for MedicineCode1
RClicks: Realistic Click Simulation for Benchmarking Interactive SegmentationCode1
GlobalMamba: Global Image Serialization for Vision MambaCode1
V2M: Visual 2-Dimensional Mamba for Image Representation LearningCode1
CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving ScenesCode1
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image SegmentationCode1
Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image SegmentationCode1
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing AttentionCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space ModelCode1
Bridge the Points: Graph-based Few-shot Segment Anything SemanticallyCode1
UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA FiltersCode1
DB-SAM: Delving into High Quality Universal Medical Image SegmentationCode1
Not All Diffusion Model Activations Have Been Evaluated as Discriminative FeaturesCode1
Med-TTT: Vision Test-Time Training model for Medical Image SegmentationCode1
Unleashing the Potential of the Diffusion Model in Few-shot Semantic SegmentationCode1
PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image SegmentationCode1
TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature GraftingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310Unverified
2ViT-P (InternImage-H)Validation mIoU63.6Unverified
3ONE-PEACEValidation mIoU63Unverified
4M3I Pre-training (InternImage-H)Validation mIoU62.9Unverified
5InternImage-HValidation mIoU62.9Unverified
6BEiT-3Validation mIoU62.8Unverified
7EVAValidation mIoU62.3Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5Unverified
10FD-SwinV2-GValidation mIoU61.4Unverified