# Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview

Source: [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](<https://www.sota2.com/research/paper/68fa6de51e209131cea3cbe4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:46:11.149Z

Authors: Wenqi Ren, Yang Tang, Qiyu Sun, Chaoqiang Zhao, Qing-Long Han

Published: 2022-11-13T13:39:33.000Z

## Abstract

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning. The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen-category from a few labeled or zero-labeled samples, which advances the extension to practical applications. Therefore, this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances. Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed. Moreover, three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation, including image semantic segmentation, video object segmentation, and 3D segmentation. Finally, the future challenges of few/zero-shot visual semantic segmentation are discussed.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.08352>)

[PDF](<https://arxiv.org/pdf/2211.08352.pdf>)