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Camouflaged Object Segmentation
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Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by T.-N. Le et al. (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on CAMO dataset, COD dataset, and online demo.
<span style="color:grey; opacity: 0.6">( Image source: Anabranch Network for Camouflaged Object Segmentation )</span>
논문
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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
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
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
ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection
Unsupervised Camouflaged Object Segmentation as Domain Adaptation
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
Camouflaged Object Detection via Context-aware Cross-level Fusion
Implicit Motion Handling for Video Camouflaged Object Detection
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Anabranch Network for Camouflaged Object Segmentation
Camouflaged Object Segmentation with Distraction Mining
Generative Transformer for Accurate and Reliable Salient Object Detection
Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite
Simultaneously Localize, Segment and Rank the Camouflaged Objects
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