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Semantic Segmentation

Papers

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Showing 8351–8400 of 14763 papers

TitleStatusHype
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
Weakly Supervised Few-Shot Segmentation Via Meta-Learning—0
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving—0
Taming Mambas for Voxel Level 3D Medical Image Segmentation—0
TA-Net: Topology-Aware Network for Gland Segmentation—0
Weakly-supervised fire segmentation by visualizing intermediate CNN layers—0
Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts—0
Tangles and Hierarchical Clustering—0
On Certified Generalization in Structured Prediction—0
OmniVec: Learning robust representations with cross modal sharing—0
Target-aware Bi-Transformer for Few-shot Segmentation—0
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation—0
OmniVDiff: Omni Controllable Video Diffusion for Generation and Understanding—0
OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation—0
OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving—0
TASeg: Text-aware RGB-T Semantic Segmentation based on Fine-tuning Vision Foundation Models—0
Task-Adaptive Feature Transformer for Few-Shot Segmentation—0
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation—0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch—0
Weakly Supervised Image Annotation and Segmentation with Objects and Attributes—0
Task-Aware Low-Rank Adaptation of Segment Anything Model—0
Task-Aware Variational Adversarial Active Learning—0
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation—0
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation—0
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation—0
Task-driven Image Fusion with Learnable Fusion Loss—0
Task-driven Prompt Evolution for Foundation Models—0
Taskology: Utilizing Task Relations at Scale—0
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images—0
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge—0
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation—0
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation—0
TAS-NIR: A VIS+NIR Dataset for Fine-grained Semantic Segmentation in Unstructured Outdoor Environments—0
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation—0
Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception—0
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators—0
MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation—0
TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation—0
TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research—0
OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation—0
OG: Equip vision occupancy with instance segmentation and visual grounding—0
Offshore Wind Plant Instance Segmentation Using Sentinel-1 Time Series, GIS, and Semantic Segmentation Models—0
Offset Curves Loss for Imbalanced Problem in Medical Segmentation—0
Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising—0
Team I2R-VI-FF Technical Report on EPIC-KITCHENS VISOR Hand Object Segmentation Challenge 2023—0
Team PFDet's Methods for Open Images Challenge 2019—0
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks—0
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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
4InternImage-HValidation mIoU62.9—Unverified
5M3I Pre-training (InternImage-H)Validation 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