Zero Shot Segmentation
37 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Zero Shot Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Libraries
Use these libraries to find Zero Shot Segmentation models and implementations
Segmentation in the Wild
TomoSAM
MatSeg
Most implemented papers
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
rt219/the-emergence-of-objectness
•
•
NeurIPS 2021
Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images.
Extract Free Dense Labels from CLIP
Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition.
Language-driven Semantic Segmentation
isl-org/lang-seg
•
•
ICLR 2022
We present LSeg, a novel model for language-driven semantic image segmentation.
Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task.
Generalized Decoding for Pixel, Image, and Language
microsoft/X-Decoder
•
•
CVPR 2023
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly.
Open-vocabulary Object Segmentation with Diffusion Models
lipurple/grounded-diffusion
•
•
ICCV 2023
The goal of this paper is to extract the visual-language correspondence from a pre-trained text-to-image diffusion model, in the form of segmentation map, i. e., simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt.
A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation
To this end, we propose a novel unified weakly supervised OVSS pipeline that can perform ZSS, FSS and Cross-dataset segmentation on novel classes without using pixel-level labels for either the base (seen) or the novel (unseen) classes in an inductive setting.
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks.
Universal Instance Perception as Object Discovery and Retrieval
MasterBin-IIAU/UNINEXT
•
•
CVPR 2023
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks.
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology.
