SOTAVerified

Image Segmentation

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

Papers

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Showing 4551–4600 of 5035 papers

TitleStatusHype
Learning Semantic Associations for Mirror Detection—0
Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy—0
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images—0
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks—0
Learning Structured Output Representations from Attributes using Deep Conditional Generative Models—0
Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization—0
Learning to Label Affordances from Simulated and Real Data—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Learning to Prompt Segment Anything Models—0
Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision—0
Learning to segment fetal brain tissue from noisy annotations—0
Learning to segment from misaligned and partial labels—0
Learning to Segment Medical Images with Scribble-Supervision Alone—0
Learning to Segment Skin Lesions from Noisy Annotations—0
Learning to segment with image-level supervision—0
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation—0
Learning Unbiased Image Segmentation: A Case Study with Plain Knee Radiographs—0
Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving—0
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation—0
Learn the new, keep the old: Extending pretrained models with new anatomy and images—0
Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans—0
Let Human Sketches Help: Empowering Challenging Image Segmentation Task with Freehand Sketches—0
Let Segment Anything Help Image Dehaze—0
Level Set KSVD—0
Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning—0
Leveraging Adaptive Implicit Representation Mapping for Ultra High-Resolution Image Segmentation—0
Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation—0
Leveraging Global Binary Masks for Structure Segmentation in Medical Images—0
Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation—0
Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation—0
LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation—0
LightFormer: A lightweight and efficient decoder for remote sensing image segmentation—0
Lightweight Monocular Depth Estimation—0
Lightweight Road Environment Segmentation using Vector Quantization—0
Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability—0
LIMIS: Towards Language-based Interactive Medical Image Segmentation—0
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation—0
Line Profile Based Segmentation Algorithm for Touching Corn Kernels—0
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation—0
Lithium Metal Battery Quality Control via Transformer-CNN Segmentation—0
Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention—0
Live-wire 3D medical images segmentation—0
LlamaSeg: Image Segmentation via Autoregressive Mask Generation—0
Local Background Estimation for Improved Gas Plume Identification in Hyperspectral Images—0
Local Binary Pattern(LBP) Optimization for Feature Extraction—0
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation—0
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation—0
Localized Randomized Smoothing for Collective Robustness Certification—0
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation—0
Locally Adaptive Learning Loss for Semantic Image Segmentation—0
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All datasetsMSD (Mirror Segmentation Dataset)PMDMAS3KPascal Panoptic PartsRMASPASCAL VOCHuTu 80MARIDACOCO val2017ImageNetMSD HeartOxfordPets

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.92—Unverified
2HetNetIoU0.83—Unverified
3PMDIoU0.82—Unverified
4SANetIoU0.8—Unverified
5MirrorNetIoU0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.73—Unverified
2HetNetIoU0.69—Unverified
3SANetIoU0.67—Unverified
4PMDIoU0.66—Unverified
5MirrorNetIoU0.59—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetmIoU0.8—Unverified
2MAS-SAMmIoU0.79—Unverified
3MASNetmIoU0.74—Unverified
4ZoomNetmIoU0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1HIPIE (ViT-H)mIoUPartS63.8—Unverified
2PPSmIoUPartS58.6—Unverified
3HIPIE (ResNet-50)mIoUPartS57.2—Unverified
4JPPFmIoUPartS54.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MAS-SAMmIoU0.74—Unverified
2SAM2-UNetmIoU0.74—Unverified
3MASNetmIoU0.73—Unverified
4ZoomNetmIoU0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CmIoU63.6—Unverified
2OneNete,4-SmAP0.552.75—Unverified
3OneNeted,4mIoU14.9—Unverified
#ModelMetricClaimedVerifiedStatus
1UNetRDice0.98—Unverified
2PALEDDice0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttUNetIoU0.67—Unverified
2UNetIoU0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1SynCo (ResNet-50) 200epmask AP35.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileOne-S0GFLOPs0.28—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4mIoU6.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CDice Score0.97—Unverified