Zero Shot Segmentation

33 papers with code • 2 benchmarks • 2 datasets

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Libraries

Use these libraries to find Zero Shot Segmentation models and implementations

Most implemented papers

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

idea-research/groundingdino 9 Mar 2023

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

timojl/clipseg CVPR 2022

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

mendelxu/san CVPR 2023

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 16 Aug 2020

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 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

xmed-lab/clip_surgery 12 Apr 2023

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

mazurowski-lab/segment-anything-medical-evaluation 20 Apr 2023

We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.

Segment Anything in High Quality

syscv/sam-hq NeurIPS 2023

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

voxelmorph/voxelmorph 25 Apr 2019

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.

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.