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Interactive Segmentation

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하위 태스크

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평가표

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보존된 평가 기록

74

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/coendevente/nninteractive-slicer

nnInteractive: Redefining 3D Promptable Segmentation

https://github.com/cy-xu/spatially_aware_ai

Segment Everything Everywhere All at Once

https://github.com/ddupont808/gpt-4v-act

Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

https://github.com/frazerlin/focuscut

https://github.com/ihomeava/PaddleSeg2

EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

https://github.com/intel-isl/Intseg

Deep Interactive Object Selection

https://github.com/jpconnel/fbrs-segmentation

f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

https://github.com/lely475/nuclick

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images

https://github.com/marcoforte/fba_matting

Getting to 99% Accuracy in Interactive Segmentation

https://github.com/marrlab/dinobloom

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images

https://github.com/mic-dkfz/lesionlocator

nnInteractive: Redefining 3D Promptable Segmentation

https://github.com/mostafajahanifar/nuclick_torch

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images

https://github.com/openmedlab/SAM-Med2D

SAM-Med2D

https://github.com/pointcept/sampart3d

SAMPart3D: Segment Any Part in 3D Objects

https://github.com/shiyinzhang/Inside-Outside-Guidance

https://github.com/shiyinzhang/Pixel-ImageNet

https://github.com/sunye23/unseg

UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation

https://github.com/supermedintel/medical-sam2

Medical SAM 2: Segment medical images as video via Segment Anything Model 2

https://github.com/supermedintel/one-prompt

One-Prompt to Segment All Medical Images

https://github.com/supervisely-ecosystem/ritm-interactive-segmentation

Reviving Iterative Training with Mask Guidance for Interactive Segmentation

https://github.com/supervisely-ecosystem/ritm_interactive_segmentation

Reviving Iterative Training with Mask Guidance for Interactive Segmentation

https://github.com/taigw/geodesic_distance

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

https://github.com/theodorakontogianni/interobject3d

Interactive Object Segmentation in 3D Point Clouds

https://github.com/uni-medical/sam-med2d

SAM-Med2D

https://github.com/ux-decoder/find

Segment Everything Everywhere All at Once

https://github.com/ux-decoder/segment-everything-everywhere-all-at-once

Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

https://github.com/xmed-lab/clipn

A Closer Look at the Explainability of Contrastive Language-Image Pre-training

https://github.com/yccyenchicheng/pytorch-SegInpaint

SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting

https://github.com/yihanhu-2022/diffmatte

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers

https://github.com/ywyue/agile3d

AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation

https://github.com/zhaoziheng/sat

One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.