# Uncertainty-Aware Prototype Semantic Decoupling for Text-Based Person Search in Full Images

arXiv: https://arxiv.org/abs/2505.03567 · HTML: https://mlsanity.com/paper/2505.03567/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [Person Search with Natural Language Description](https://mlsanity.com/paper/1702.05729/) (2017)
- [Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2303.12501/) (2023)
- [Anchor-Free Person Search](https://mlsanity.com/paper/2103.11617/) (2021)
- [Context Disentangling and Prototype Inheriting for Robust Visual Grounding](https://mlsanity.com/paper/2312.11967/) (2023)
- [MARS: Paying more attention to visual attributes for text-based person search](https://mlsanity.com/paper/2407.04287/) (2024)
- [Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture](https://mlsanity.com/paper/2210.02676/) (2022)
- [Text-based Person Search in Full Images via Semantic-Driven Proposal Generation](https://mlsanity.com/paper/2109.12965/) (2021)
- [Prototypical Prompting for Text-to-image Person Re-identification](https://mlsanity.com/paper/2409.09427/) (2024)

## Built on by (most cited first, 1 of 1)

- [FitPro: A Zero-Shot Framework for Interactive Text-based Pedestrian Retrieval in Open World](https://mlsanity.com/paper/2509.16674/) (2025)

## Abstract (arXiv)

Text-based pedestrian search (TBPS) in full images aims to locate a target pedestrian in untrimmed images using natural language descriptions. However, in complex scenes with multiple pedestrians, existing methods are limited by uncertainties in detection and matching, leading to degraded performance. To address this, we propose UPD-TBPS, a novel framework comprising three modules: Multi-granularity Uncertainty Estimation (MUE), Prototype-based Uncertainty Decoupling (PUD), and Cross-modal Re-identification (ReID). MUE conducts multi-granularity queries to identify potential targets and assigns confidence scores to reduce early-stage uncertainty. PUD leverages visual context decoupling and prototype mining to extract features of the target pedestrian described in the query. It separates and learns pedestrian prototype representations at both the coarse-grained cluster level and the fine-grained individual level, thereby reducing matching uncertainty. ReID evaluates candidates with varying confidence levels, improving detection and retrieval accuracy. Experiments on CUHK-SYSU-TBPS and PRW-TBPS datasets validate the effectiveness of our framework.