Zero Shot Segmentation
36 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
Most implemented papers
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
idea-research/groundingdino
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To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Image Segmentation Using Text and Image Prompts
After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.
Side Adapter Network for Open-Vocabulary Semantic Segmentation
A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks.
Context-aware Feature Generation for Zero-shot Semantic Segmentation
bcmi/CaGNet-Zero-Shot-Semantic-Segmentation
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In this paper, we propose a novel context-aware feature generation method for zero-shot segmentation named CaGNet.
A Simple Framework for Open-Vocabulary Segmentation and Detection
idea-research/openseed
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ICCV 2023
We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets.
CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Contrastive Language-Image Pre-training (CLIP) is a powerful multimodal large vision model that has demonstrated significant benefits for downstream tasks, including many zero-shot learning and text-guided vision tasks.
Segment Anything Model for Medical Image Analysis: an Experimental Study
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
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.
Segment Anything in High Quality
HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
