Zero Shot Segmentation
37 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Zero Shot Segmentation
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Libraries
Use these libraries to find Zero Shot Segmentation models and implementations
Segmentation in the Wild
TomoSAM
MatSeg
Latest papers with no code
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts.
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
Rapid advancements in continual segmentation have yet to bridge the gap of scaling to large continually expanding vocabularies under compute-constrained scenarios.
Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models.
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
(1) Mutually-Refined Proposal Extraction.
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning.
Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation
We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans.
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision
Recently, learning open-vocabulary semantic segmentation from text supervision has achieved promising downstream performance.
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
This unsupervised approach allows the generated data to capture the vast complexity of the real world while maintaining the precision and scale of synthetic data.
From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments
We argue that SAM drastically over-segment images with high corruption levels, resulting in degraded performance when only a single segmentation mask is considered, while the combination of the masks overlapping the object of interest generates an accurate prediction.
Increasing SAM Zero-Shot Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
This study develops and evaluates a novel multimodal medical image zero-shot segmentation algorithm named Text-Visual-Prompt SAM (TV-SAM) without any manual annotations.
