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Image Segmentation
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Rethinking Query-based Transformer for Continual Image Segmentation
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations
RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts
Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
No time to train! Training-Free Reference-Based Instance Segmentation
DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback Synergy
Autoadaptive Medical Segment Anything Model
Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays
MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts
Segment Anything in Pathology Images with Natural Language
MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification
EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis
Mixtures of Neural Cellular Automata: A Stochastic Framework for Growth Modelling and Self-Organization
AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus
MedSeg-R: Medical Image Segmentation with Clinical Reasoning
Mobile Image Analysis Application for Mantoux Skin Test
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
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