Zero-Shot Semantic Segmentation

17 papers with code • 4 benchmarks • 3 datasets

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Most implemented papers

Zero-Shot Semantic Segmentation

valeoai/ZS3 NeurIPS 2019

Semantic segmentation models are limited in their ability to scale to large numbers of object classes.

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 Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model

mendelxu/zsseg.baseline 29 Dec 2021

However, semantic segmentation and the CLIP model perform on different visual granularity, that semantic segmentation processes on pixels while CLIP performs on images.

Learning unbiased zero-shot semantic segmentation networks via transductive transfer

ycccccccccc/Learning-unbiased-zero-shot-semantic-segmentation-networks-via-transductive-transfer 1 Jul 2020

Our method assumes that both the source images with full pixel-level labels and unlabeled target images are available during training.

From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation

bcmi/CaGNetv2-Zero-Shot-Semantic-Segmentation 25 Sep 2020

Thus, we focus on zero-shot semantic segmentation, which aims to segment unseen objects with only category-level semantic representations provided for unseen categories.

Extract Free Dense Labels from CLIP

chongzhou96/maskclip 2 Dec 2021

Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition.

Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models

machengcheng2016/Subspace-Prompt-Learning 4 Nov 2022

Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts.

ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation

ZiqinZhou66/ZegCLIP CVPR 2023

Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme.

Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware Synthesis

leolyj/3dpc-gzsl ICCV 2023

Given only the class-level semantic information for unseen objects, we strive to enhance the correspondence, alignment and consistency between the visual and semantic spaces, to synthesise diverse, generic and transferable visual features.