Papers Interactive Segmentation
“Interactive Segmentation” 태그가 달린 논문 306편 · 필터 해제
VILLS -- Video-Image Learning to Learn Semantics for Person Re-Identification
Person Re-identification is a research area with significant real world applications. Despite recent progress, existing methods face challenges in robust re-identification in the wild, e.g., by focusing only on a particu…
Interactive SegmentationPerson Re-IdentificationSelf-Supervised LearningSliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations. This requirement presents a challenge f…
Interactive SegmentationSegmentationDeep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or ma…
Interactive SegmentationMedical Image AnalysisAssessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
Interactive segmentation model leverages prompts from users to produce robust segmentation. This advancement is facilitated by prompt engineering, where interactive prompts serve as strong priors during test-time. Howeve…
Image SegmentationInteractive SegmentationMedical Image SegmentationPrompt Engineering+3Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V. As illustrated in Fig. 1 (right), we employ off-the-shelf interact…
Interactive SegmentationReferring ExpressionReferring Expression ComprehensionSegmentation+2Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation
With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potent…
DecoderImage SegmentationInteractive SegmentationSegmentation+1DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces
Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between training and testing data affect their …
Deep LearningInteractive SegmentationSegmentationAdapting Segment Anything Model for Change Detection in HR Remote Sensing Images
Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety of visual scenes. However, their direct …
Change DetectionInteractive SegmentationSAM-Med2D
The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points and bounding boxes. However, our evaluatio…
DecoderImage SegmentationInteractive SegmentationMedical Image Segmentation+2Deep learning-based interactive segmentation in remote sensing
Interactive segmentation, a computer vision technique where a user provides guidance to help an algorithm segment a feature of interest in an image, has achieved outstanding accuracy and efficient human-computer interact…
Deep LearningInteractive SegmentationLand Cover ClassificationSegmentation+1TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot
Tongue segmentation serves as the primary step in automated TCM tongue diagnosis, which plays a significant role in the diagnostic results. Currently, numerous deep learning based methods have achieved promising results.…
DiagnosticInteractive Segmentationobject-detectionObject Detection+3Scene-Generalizable Interactive Segmentation of Radiance Fields
Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their applicability. In this work we make the f…
Interactive SegmentationSegmentationSemantic SegmentationFeature Decoupling-Recycling Network for Fast Interactive Segmentation
Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the source image. As a result, extracting featur…
Image SegmentationInteractive SegmentationMedical Image SegmentationSegmentation+1Interactive Segmentation for Diverse Gesture Types Without Context
Interactive segmentation entails a human marking an image to guide how a model either creates or edits a segmentation. Our work addresses limitations of existing methods: they either only support one gesture type for mar…
Interactive SegmentationSegmentationInteractive Image Segmentation with Cross-Modality Vision Transformers
Interactive image segmentation aims to segment the target from the background with the manual guidance, which takes as input multimodal data such as images, clicks, scribbles, and bounding boxes. Recently, vision transfo…
Image SegmentationInteractive SegmentationSegmentationSemantic SegmentationCGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement
Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. Howe…
Image SegmentationInteractive SegmentationSegmentationSemantic SegmentationPVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation
Integration of diverse visual prompts like clicks, scribbles, and boxes in interactive image segmentation significantly facilitates users' interaction as well as improves interaction efficiency. However, existing studies…
Image SegmentationInteractive SegmentationSegmentationSemantic SegmentationR-MAE: Regions Meet Masked Autoencoders
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-training baseline, we propose masked reg…
Contrastive LearningInteractive Segmentationobject-detectionObject Detection+2AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point to an object (or the background), while t…
Binary ClassificationInteractive SegmentationObjectSegmentationBreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images
Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic tool that aids in early detection by provid…
DiagnosticInteractive SegmentationSegmentationTumor Segmentation