Papers Camouflaged Object Segmentation
“Camouflaged Object Segmentation” 태그가 달린 논문 52편 · 필터 해제
Discover, Segment, and Select: A Progressive Mechanism for Zero-shot Camouflaged Object Segmentation
Current zero-shot Camouflaged Object Segmentation methods typically employ a two-stage pipeline (discover-then-segment): using MLLMs to obtain visual prompts, followed by SAM segmentation. However, relying solely on MLLM…
Camouflaged Object SegmentationContext-measure: Contextualizing Metric for Camouflage
Camouflage relies heavily on context, but current metrics used in camouflaged object segmentation ignore contextual cues. We identify two major drawbacks of these metrics: first, the Dimension Flaw - a predicted foregrou…
Camouflaged Object SegmentationClassifier-Centric Adaptive Framework for Open-Vocabulary Camouflaged Object Segmentation
Open-vocabulary camouflaged object segmentation requires models to segment camouflaged objects of arbitrary categories unseen during training, placing extremely high demands on generalization capabilities. Through analys…
Camouflaged Object SegmentationArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Camouflaged Object Segmentation (COS) poses a significant challenge due to the intrinsic high similarity between targets and backgrounds, demanding models capable of profound holistic understanding beyond superficial cue…
Camouflaged Object SegmentationMedical Image SegmentationScene UnderstandingAn Instance-Aware Prompting Framework for Training-free Camouflaged Object Segmentation
Training-free Camouflaged Object Segmentation (COS) seeks to segment camouflaged objects without task-specific training, by automatically generating visual prompts to guide the Segment Anything Model (SAM). However, exis…
Camouflaged Object SegmentationDC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
Interactive segmentation (IS) allows users to iteratively refine object boundaries with minimal cues, such as positive and negative clicks. While the Segment Anything Model (SAM) has garnered attention in the IS communit…
Camouflaged Object SegmentationInteractive SegmentationSegmentationSemantic Segmentation+1Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models
Open-Vocabulary Camouflaged Object Segmentation (OVCOS) seeks to segment and classify camouflaged objects from arbitrary categories, presenting unique challenges due to visual ambiguity and unseen categories.Recent appro…
Camouflaged Object SegmentationSegmentationSemantic SegmentationStepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation
While promptable segmentation (\textit{e.g.}, SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for each object to be segmented. In contrast, task-generic promptable segmentat…
Camouflaged Object SegmentationFeature CorrelationImage CaptioningSegmentation+3ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation
Camouflaged object segmentation presents unique challenges compared to traditional segmentation tasks, primarily due to the high similarity in patterns and colors between camouflaged objects and their backgrounds. Effect…
Camouflaged Object SegmentationDefect DetectionLesion SegmentationOptical Flow Estimation+3CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection
The Segment Anything Model 2 (SAM2), a prompt-guided video foundation model, has remarkably performed in video object segmentation, drawing significant attention in the community. Due to the high similarity between camou…
Camouflaged Object Segmentationobject-detectionObject DetectionSemantic Segmentation+2ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot Method
Camouflaged object segmentation presents unique challenges compared to traditional segmentation tasks, primarily due to the high similarity in patterns and colors between camouflaged objects and their backgrounds. Effect…
Camouflaged Object SegmentationDefect DetectionLesion SegmentationOptical Flow Estimation+3CamSAM2: Segment Anything Accurately in Camouflaged Videos
Video camouflaged object segmentation (VCOS), aiming at segmenting camouflaged objects that seamlessly blend into their environment, is a fundamental vision task with various real-world applications. With the release of …
Camouflaged Object SegmentationObjectSemantic SegmentationVideo Segmentation+1Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
Camouflaged Object Segmentation (COS) remains challenging because camouflaged objects exhibit only subtle visual differences from their backgrounds and single-modality RGB methods provide limited cues, leading researcher…
Camouflaged Object SegmentationSegmentationSemantic SegmentationFOCUS: Towards Universal Foreground Segmentation
Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unificati…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationSalient Object Detection+2Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering
In this work, we introduce Camouflage Anything, a novel and robust approach to generate camouflaged datasets. To the best of our knowledge, we are the first to apply Controlled Out-painting and Representation Enginee…
Camouflaged Object SegmentationObjectSemantic SegmentationTowards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations
Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly, primarily due to the intricate object-…
Camouflaged Object SegmentationLarge Language ModelMultimodal Large Language ModelNavigate+3When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessl…
Camouflaged Object SegmentationSemantic SegmentationLeveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation
Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segmentation has been introduced, which emplo…
Camouflaged Object SegmentationCamouflaged Object Segmentation with a Single Task-generic PromptMedical Image SegmentationSegmentationUnlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy
In the domain of Camouflaged Object Segmentation (COS), despite continuous improvements in segmentation performance, the underlying mechanisms of effective camouflage remain poorly understood, akin to a black box. To add…
AttributeCamouflaged Object SegmentationSemantic SegmentationLearning Camouflaged Object Detection from Noisy Pseudo Label
Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive. Although weakly supervised methods offer higher annotation…
Camouflaged Object SegmentationMemorizationObjectobject-detection+3