# MARS: Paying more attention to visual attributes for text-based person search arXiv: https://arxiv.org/abs/2407.04287 ยท HTML: https://mlsanity.com/paper/2407.04287/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 30 references that are in the map. - First posted on arXiv 5 Jul 2024. ## Builds on (most cited first, 20 of 20) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021) - [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) - [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021) - [DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2109.05534/) (2021) - [ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language](https://mlsanity.com/paper/2005.07327/) (2020) - [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023) - [See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval](https://mlsanity.com/paper/2208.08608/) (2022) - [Learning Granularity-Unified Representations for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2207.07802/) (2022) - [RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search](https://mlsanity.com/paper/2305.13653/) (2023) - [An Empirical Study of CLIP for Text-based Person Search](https://mlsanity.com/paper/2308.10045/) (2023) - [Text-based Person Search without Parallel Image-Text Data](https://mlsanity.com/paper/2305.12964/) (2023) - [From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search](https://mlsanity.com/paper/2404.10292/) (2024) - [VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search](https://mlsanity.com/paper/2311.07514/) (2023) - [Cross-Modal Adaptive Dual Association for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2312.01745/) (2023) ## Built on by (most cited first, 11 of 11) - [ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models](https://mlsanity.com/paper/2502.19958/) (2025) - [HumanVLM: Foundation for Human-Scene Vision-Language Model](https://mlsanity.com/paper/2411.03034/) (2024) - [CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2504.18782/) (2025) - [ITSELF: Attention Guided Fine-Grained Alignment for Vision-Language Retrieval](https://mlsanity.com/paper/2601.01024/) (2026) - [Uncertainty-Aware Prototype Semantic Decoupling for Text-Based Person Search in Full Images](https://mlsanity.com/paper/2505.03567/) (2025) - [SA-Person: Text-Based Person Retrieval with Scene-aware Re-ranking](https://mlsanity.com/paper/2505.24466/) (2025) - [Cross-modal Full-mode Fine-grained Alignment for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2509.13754/) (2025) - [FitPro: A Zero-Shot Framework for Interactive Text-based Pedestrian Retrieval in Open World](https://mlsanity.com/paper/2509.16674/) (2025) - [InterPartAbility: Phrase-Region Grounding for Interpretable Text-to-Image Person Re-Identification](https://mlsanity.com/paper/2604.27122/) (2026) - [T2I-VeRW: Part-level Fine-grained Perception for Text-to-Image Vehicle Retrieval](https://mlsanity.com/paper/2605.06012/) (2026) - [Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification](https://mlsanity.com/paper/2606.02242/) (2026) ## Abstract (arXiv) Text-based person search (TBPS) is a problem that gained significant interest within the research community. The task is that of retrieving one or more images of a specific individual based on a textual description. The multi-modal nature of the task requires learning representations that bridge text and image data within a shared latent space. Existing TBPS systems face two major challenges. One is defined as inter-identity noise that is due to the inherent vagueness and imprecision of text descriptions and it indicates how descriptions of visual attributes can be generally associated to different people; the other is the intra-identity variations, which are all those nuisances e.g. pose, illumination, that can alter the visual appearance of the same textual attributes for a given subject. To address these issues, this paper presents a novel TBPS architecture named MARS (Mae-Attribute-Relation-Sensitive), which enhances current state-of-the-art models by introducing two key components: a Visual Reconstruction Loss and an Attribute Loss. The former employs a Masked AutoEncoder trained to reconstruct randomly masked image patches with the aid of the textual description. In doing so the model is encouraged to learn more expressive representations and textual-visual relations in the latent space. The Attribute Loss, instead, balances the contribution of different types of attributes, defined as adjective-noun chunks of text. This loss ensures that every attribute is taken into consideration in the person retrieval process. Extensive experiments on three commonly used datasets, namely CUHK-PEDES, ICFG-PEDES, and RSTPReid, report performance improvements, with significant gains in the mean Average Precision (mAP) metric w.r.t. the current state of the art.