SOTAVerified

Zero Shot Segmentation

Papers

Showing 125 of 134 papers

TitleStatusHype
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A SurveyCode5
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object DetectionCode5
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive ReinforcementCode4
A Simple Framework for Open-Vocabulary Segmentation and DetectionCode3
ZIM: Zero-Shot Image Matting for AnythingCode3
Universal Instance Perception as Object Discovery and RetrievalCode3
RobustSAM: Segment Anything Robustly on Degraded ImagesCode3
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
Generalized Decoding for Pixel, Image, and LanguageCode3
Interpreting and Editing Vision-Language Representations to Mitigate HallucinationsCode2
Language-driven Semantic SegmentationCode2
Hierarchical Open-vocabulary Universal Image SegmentationCode2
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationCode2
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized CutCode2
Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable DiffusionCode2
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image SegmentationCode2
Side Adapter Network for Open-Vocabulary Semantic SegmentationCode2
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion ModelsCode2
MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image SegmentationCode2
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation ModelsCode2
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image SegmentationCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object RearrangementCode2
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human AnnotationCode1
How to Efficiently Adapt Large Segmentation Model(SAM) to Medical ImagesCode1
Show:102550
← PrevPage 1 of 6Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Grounded HQ-SAMMean AP49.6Unverified
2Grounded-SAMMean AP46Unverified
3UNINEXTMean AP42.1Unverified
4HIPIEMean AP41.6Unverified
5SANMean AP41.4Unverified
6odiseMean AP38.7Unverified
7OpenSEEDMean AP36.1Unverified
8OpenSDMean AP35.8Unverified
9SGinW_Team (X-Decoder-L)Mean AP32.2Unverified
10SGinW_Team (X-Decoder-B)Mean AP27.7Unverified
#ModelMetricClaimedVerifiedStatus
1COSMOS ViT-B/16mIoU17.7Unverified
2GEM (MetaCLIP)mIoU17.1Unverified
3GEM (CLIP)mIoU15.7Unverified
4CLIPSurgerymIoU12.9Unverified
5MaskCLIPmIoU10.2Unverified