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Semantic Segmentation

Papers

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Showing 8901–8925 of 14763 papers

TitleStatusHype
Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation—0
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation—0
Training and Inference for Integer-Based Semantic Segmentation Network—0
MGTUNet: An new UNet for colon nuclei instance segmentation and quantification—0
Training Bit Fully Convolutional Network for Fast Semantic Segmentation—0
Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary—0
Auxiliary Learning for Deep Multi-task Learning—0
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation—0
Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations—0
Training Deep Networks to be Spatially Sensitive—0
MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced Medical Image Segmentation—0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
MVTec D2S: Densely Segmented Supermarket Dataset—0
WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity—0
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization—0
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation—0
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts—0
Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation—0
Training-Free Robust Interactive Video Object Segmentation—0
Training-Free Semantic Segmentation via LLM-Supervision—0
MFSeg: Efficient Multi-frame 3D Semantic Segmentation—0
Training Group Orthogonal Neural Networks with Privileged Information—0
MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds—0
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound—0
Training Object Detectors From Scratch: An Empirical Study in the Era of Vision Transformer—0
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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
4M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
5InternImage-HValidation 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