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Object Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models
Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation
CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot Method
CamSAM2: Segment Anything Accurately in Camouflaged Videos
Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
Auto-Prompting SAM for Weakly Supervised Landslide Extraction
FOCUS: Towards Universal Foreground Segmentation
Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations
When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation
Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy
Learning Camouflaged Object Detection from Noisy Pseudo Label
TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects
Open-Vocabulary Camouflaged Object Segmentation
Estimating Uncertainty in Landslide Segmentation Models
ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection
Unsupervised Camouflaged Object Segmentation as Domain Adaptation
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection
Diffusion Model for Camouflaged Object Detection
Improving existing segmentators performance with zero-shot segmentators
Edge-Aware Mirror Network for Camouflaged Object Detection
Explicit Visual Prompting for Universal Foreground Segmentations
The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
Explicit Visual Prompting for Low-Level Structure Segmentations
DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering
SAR-based landslide classification pretraining leads to better segmentation
Camouflaged Object Detection via Context-aware Cross-level Fusion
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
Implicit Motion Handling for Video Camouflaged Object Detection
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
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