Papers Interactive Segmentation
“Interactive Segmentation” 태그가 달린 논문 306편 · 필터 해제
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans
Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaptation in medical image segmentation tasks shows significant performance drops. It also requi…
GPUImage SegmentationInteractive SegmentationMedical Image Segmentation+2IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence
Training a computer vision system to segment a novel class typically requires collecting and painstakingly annotating lots of images with objects from that class. Few-shot segmentation techniques reduce the required numb…
Interactive SegmentationSegmentationLeveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?
Interactive segmentation, an integration of AI algorithms and human expertise, premises to improve the accuracy and efficiency of curating large-scale, detailed-annotated datasets in healthcare. Human experts revise the …
Interactive SegmentationQIS : Interactive Segmentation via Quasi-Conformal Mappings
Image segmentation plays a crucial role in extracting important objects of interest from images, enabling various applications. While existing methods have shown success in segmenting clean images, they often struggle to…
Image SegmentationInteractive SegmentationSegmentationSemantic SegmentationMinimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's clinical workflow, while automatic segment…
Interactive SegmentationSegmentationTETRIS: Towards Exploring the Robustness of Interactive Segmentation
Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most co…
Adversarial AttackAdversarial RobustnessCommon Sense ReasoningInteractive Segmentation+1OMG-Seg: Is One Model Good Enough For All Segmentation?
In this work, we address various segmentation tasks, each traditionally tackled by distinct or partially unified models. We propose OMG-Seg, One Model that is Good enough to efficiently and effectively handle all the seg…
AllDecoderInteractive SegmentationPanoptic Segmentation+4RAP-SAM: Towards Real-Time All-Purpose Segment Anything
Advanced by transformer architecture, vision foundation models (VFMs) achieve remarkable progress in performance and generalization ability. Segment Anything Model (SAM) is one remarkable model that can achieve generaliz…
AllDecoderInteractive SegmentationPanoptic Segmentation+4A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting
Panoptic and instance segmentation networks are often trained with specialized object detection modules, complex loss functions, and ad-hoc post-processing steps to manage the permutation-invariance of the instance masks…
Instance SegmentationInteractive Segmentationobject-detectionObject Detection+3MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation
Interactive segmentation has gained significant attention for its application in human-computer interaction and data annotation. To address the target scale variation issue in interactive segmentation, a novel multi-scal…
BenchmarkingInteractive SegmentationSegmentationOpen-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its zero-shot recognition capabilities. This…
image-classificationImage ClassificationInteractive SegmentationSegmentation+2Rethinking Interactive Image Segmentation with Low Latency High Quality and Diverse Prompts
The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation with diverse prompts remain challengin…
Image SegmentationInteractive SegmentationSegmentationSemantic SegmentationOne Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts
In this study, we aim to build up a model that can Segment Anything in radiology scans, driven by medical terminologies as Text prompts, termed as SAT. Our main contributions are three folds: (i) for dataset construction…
AllAnatomyContrastive LearningInteractive Segmentation+5SplatMesh: Interactive 3D Segmentation and Editing Using Mesh-Based Gaussian Splatting
A key challenge in fine-grained 3D-based interactive editing is the absence of an efficient representation that balances diverse modifications with high-quality view synthesis under a given memory constraint. While 3D me…
Interactive SegmentationSegmentationScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image
Biomedical image segmentation is a crucial part of both scientific research and clinical care. With enough labelled data, deep learning models can be trained to accurately automate specific biomedical image segmentation …
Image SegmentationInteractive SegmentationMedical Image SegmentationSegmentation+1A dynamic interactive learning framework for automated 3D medical image segmentation
Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration. We propose a dynamic inte…
Image RegistrationImage SegmentationInteractive SegmentationKnowledge Distillation+3SqueezeSAM: User friendly mobile interactive segmentation
The Segment Anything Model (SAM) has been a cornerstone in the field of interactive segmentation, propelling significant progress in generative AI, computational photography, and medical imaging. Despite its ability to p…
Data AugmentationInteractive SegmentationObjectobject-detection+3SAMBA: A Trainable Segmentation Web-App with Smart Labelling
Segmentation is the assigning of a semantic class to every pixel in an image and is a prerequisite for various statistical analysis tasks in materials science, like phase quantification, physics simulations or morphologi…
Interactive SegmentationSegmentationThe Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model
Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requ…
Image RetrievalInteractive SegmentationLanguage ModelingLanguage Modelling+1Segment Any 3D Gaussians
This paper presents SAGA (Segment Any 3D GAussians), a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS). Given 2D visual prompts as input, SAGA can segment the corresponding 3D ta…
Interactive SegmentationScene UnderstandingSegmentation