SOTAVerified

Segmentation

Papers

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Showing 11451–11475 of 13072 papers

TitleStatusHype
LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation—0
LNDb: A Lung Nodule Database on Computed Tomography—0
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification—0
Local block-wise self attention for normal organ segmentation—0
Local Context Attention for Salient Object Segmentation—0
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation—0
A Characteristic Function-based Algorithm for Geodesic Active Contours—0
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation—0
A Facial Feature Discovery Framework for Race Classification Using Deep Learning—0
OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation—0
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation—0
Localized Interactive Instance Segmentation—0
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation—0
Locally Adaptive Learning Loss for Semantic Image Segmentation—0
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition—0
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network—0
AF_2: Adaptive Focus Framework for Aerial Imagery Segmentation—0
3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues—0
Locate then Segment: A Strong Pipeline for Referring Image Segmentation—0
Location-Aware Self-Supervised Transformers for Semantic Segmentation—0
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views—0
Open-NeRF: Towards Open Vocabulary NeRF Decomposition—0
Open-RGBT: Open-vocabulary RGB-T Zero-shot Semantic Segmentation in Open-world Environments—0
LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images—0
LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery—0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8—Unverified
2SAMAverage Precision38.9—Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20—Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69—Unverified