# Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation

Source: [Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation](<https://www.sota2.com/research/paper/68fa7a101e209131cea80e5e>)

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

Data updated: 2025-10-28T13:57:19.237Z

Authors: Sunghyun Park, Jungsoo Lee, Shubhankar Borse, Munawar Hayat, Sungha Choi, Kyuwoong Hwang, Fatih Porikli

Published: 2025-07-15T06:51:07.000Z

## Abstract

While open-vocabulary semantic segmentation (OVSS) can segment an image into semantic regions based on arbitrarily given text descriptions even for classes unseen during training, it fails to understand personal texts (e.g., \`my mug cup') for segmenting regions of specific interest to users. This paper addresses challenges like recognizing \`my mug cup' among \`multiple mug cups'. To overcome this challenge, we introduce a novel task termed \\textit{personalized open-vocabulary semantic segmentation} and propose a text prompt tuning-based plug-in method designed to recognize personal visual concepts using a few pairs of images and masks, while maintaining the performance of the original OVSS. Based on the observation that reducing false predictions is essential when applying text prompt tuning to this task, our proposed method employs \`negative mask proposal' that captures visual concepts other than the personalized concept. We further improve the performance by enriching the representation of text prompts by injecting visual embeddings of the personal concept into them. This approach enhances personalized OVSS without compromising the original OVSS performance. We demonstrate the superiority of our method on our newly established benchmarks for this task, including FSS$^\\text{per}$, CUB$^\\text{per}$, and ADE$^\\text{per}$.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.11030>)

[PDF](<https://arxiv.org/pdf/2507.11030.pdf>)