SOTAVerified

Semantic Segmentation

Papers

Showing 24762500 of 14763 papers

TitleStatusHype
vMFNet: Compositionality Meets Domain-generalised SegmentationCode1
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image SegmentationCode1
SARNet: Semantic Augmented Registration of Large-Scale Urban Point CloudsCode1
LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic SegmentationCode1
Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image SegmentationCode1
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub RobotCode1
Explicitly incorporating spatial information to recurrent networks for agricultureCode1
CV 3315 Is All You Need : Semantic Segmentation CompetitionCode1
Evidence fusion with contextual discounting for multi-modality medical image segmentationCode1
SemMAE: Semantic-Guided Masking for Learning Masked AutoencodersCode1
Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA ImagesCode1
MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot SegmentationCode1
REVECA -- Rich Encoder-decoder framework for Video Event CAptionerCode1
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image SegmentationCode1
Learning Implicit Feature Alignment Function for Semantic SegmentationCode1
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task LearningCode1
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image SegmentationCode1
Balancing Discriminability and Transferability for Source-Free Domain AdaptationCode1
Online Segmentation of LiDAR Sequences: Dataset and AlgorithmCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
Patch-level Representation Learning for Self-supervised Vision TransformersCode1
Simple and Efficient Architectures for Semantic SegmentationCode1
Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and SegmentationCode1
How to Reduce Change Detection to Semantic SegmentationCode1
Masked Frequency Modeling for Self-Supervised Visual Pre-TrainingCode1
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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