Interactive Segmentation
65 papers with code • 14 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Interactive Segmentation
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Libraries
Use these libraries to find Interactive Segmentation models and implementations
MS COCO
Cityscapes
DAVIS
PASCAL VOC
SBD
ssTEM
DAVIS-585
Latest papers
Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
matt3o/autopet2-submission
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Deep learning has revolutionized the accurate segmentation of diseases in medical imaging.
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts.
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
ux-decoder/segment-everything-everywhere-all-at-once
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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.
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
ggsding/sam-cd
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In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs).
SAM-Med2D
To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot
To address this issue, this paper proposes a universal tongue segmentation model named TongueSAM based on SAM (Segment Anything Model).
Interactive 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.
R-MAE: Regions Meet Masked Autoencoders
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning.
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
One-Prompt to Segment All Medical Images
Tested on 14 previously unseen datasets, the One-Prompt Model showcases superior zero-shot segmentation capabilities, outperforming a wide range of related methods.
