https://github.com/520xyxyzq/3dgs-cd
공식 코드3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement
Papers with Code 아카이브
Zero Shot Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AngeLouCN/SAM-2_Surgical_Video
공식 코드Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2
https://github.com/ExplainableML/cosmos
공식 코드COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
https://github.com/HealthX-Lab/MedCLIP-SAM
공식 코드MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
https://github.com/MasterBin-IIAU/UNINEXT
공식 코드Universal Instance Perception as Object Discovery and Retrieval
https://github.com/PJLallen/InstanceSAM2Eval
공식 코드Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation
https://github.com/aleemsidra/SaLIP
공식 코드Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation
공식 코드Context-aware Feature Generation for Zero-shot Semantic Segmentation
https://github.com/benjamintowle/simsam
공식 코드SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction
https://github.com/berkeley-hipie/hipie
공식 코드https://github.com/blessing988/pavecap
공식 코드https://github.com/chongzhou96/maskclip
공식 코드https://github.com/clearxu/spt
공식 코드Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic Segmentation
https://github.com/cshan-github/tonguesam
공식 코드TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot
https://github.com/dvlab-research/Seg-Zero
공식 코드Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/fsemerar/slicertomosam
공식 코드TomoSAM: a 3D Slicer extension using SAM for tomography segmentation
https://github.com/google/diffseg
공식 코드Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion
https://github.com/healthx-lab/medclip-samv2
공식 코드MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation
https://github.com/heshuting555/PADing
공식 코드Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
https://github.com/hhankyangg/sam-unet
공식 코드SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
https://github.com/hwei-hw/generalist_vision_foundation_models_for_medical_imaging
공식 코드https://github.com/idea-research/groundingdino
공식 코드Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/idea-research/openseed
공식 코드A Simple Framework for Open-Vocabulary Segmentation and Detection
https://github.com/isl-org/lang-seg
공식 코드https://github.com/jiaosiyu1999/maft
공식 코드Learning Mask-aware CLIP Representations for Zero-Shot Segmentation
https://github.com/juntongkuki/birkhoff-model-compression
공식 코드https://github.com/jzk00/tv-sam
공식 코드https://github.com/kakaobrain/tcl
공식 코드https://github.com/kidswithtokens/one-prompt
공식 코드https://github.com/kimsekeun/medivista-sam
공식 코드MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
https://github.com/krtit/semanticregen
공식 코드Removing Watermarks with Partial Regeneration using Semantic Information
https://github.com/lexieyang/3d-pointzshots
공식 코드https://github.com/mazurowski-lab/sam-texturalconfusion-metrics
공식 코드https://github.com/mazurowski-lab/segment-anything-medical
공식 코드Segment Anything Model for Medical Image Analysis: an Experimental Study
https://github.com/mazurowski-lab/segment-anything-medical-evaluation
공식 코드Segment Anything Model for Medical Image Analysis: an Experimental Study
https://github.com/mendelxu/san
공식 코드Side Adapter Network for Open-Vocabulary Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.