# BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification

arXiv: https://arxiv.org/abs/2309.04675 · HTML: https://mlsanity.com/paper/2309.04675/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 28 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts](https://mlsanity.com/paper/1406.2031/) (2014)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [Person Search with Natural Language Description](https://mlsanity.com/paper/1702.05729/) (2017)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (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)
- [Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing](https://mlsanity.com/paper/1703.05446/) (2017)
- [DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2109.05534/) (2021)
- [CLIP-Driven Fine-grained Text-Image Person Re-identification](https://mlsanity.com/paper/2210.10276/) (2022)
- [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)
- [TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search](https://mlsanity.com/paper/2105.11628/) (2021)
- [RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search](https://mlsanity.com/paper/2305.13653/) (2023)
- [Deep Human Parsing with Active Template Regression](https://mlsanity.com/paper/1503.02391/) (2015)
- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [Masked Image Modeling with Local Multi-Scale Reconstruction](https://mlsanity.com/paper/2303.05251/) (2023)
- [Learning Semantic-Aligned Feature Representation for Text-based Person Search](https://mlsanity.com/paper/2112.06714/) (2021)
- [How to Understand Masked Autoencoders](https://mlsanity.com/paper/2202.03670/) (2022)
- [Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search](https://mlsanity.com/paper/2208.14365/) (2022)
- [Look Before You Leap: Improving Text-based Person Retrieval by Learning A Consistent Cross-modal Common Manifold](https://mlsanity.com/paper/2209.06209/) (2022)
- [AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification](https://mlsanity.com/paper/2101.08238/) (2021)

## Built on by (most cited first, 8 of 8)

- [All in One Framework for Multimodal Re-identification in the Wild](https://mlsanity.com/paper/2405.04741/) (2024)
- [CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2504.18782/) (2025)
- [Knowing Where to Focus: Attention-Guided Alignment for Text-based Person Search](https://mlsanity.com/paper/2412.15106/) (2024)
- [ITSELF: Attention Guided Fine-Grained Alignment for Vision-Language Retrieval](https://mlsanity.com/paper/2601.01024/) (2026)
- [AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2409.06385/) (2024)
- [An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval](https://mlsanity.com/paper/2503.22171/) (2025)
- [ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search](https://mlsanity.com/paper/2604.12762/) (2026)
- [Achieving Text-based Person Retrieval with Any Granularity](https://mlsanity.com/paper/2607.21057/) (2026)

## Abstract (arXiv)

Text-based person re-identification (TBPReID) aims to retrieve person images represented by a given textual query. In this task, how to effectively align images and texts globally and locally is a crucial challenge. Recent works have obtained high performances by solving Masked Language Modeling (MLM) to align image/text parts. However, they only performed uni-directional (i.e., from image to text) local-matching, leaving room for improvement by introducing opposite-directional (i.e., from text to image) local-matching. In this work, we introduce Bidirectional Local-Matching (BiLMa) framework that jointly optimize MLM and Masked Image Modeling (MIM) in TBPReID model training. With this framework, our model is trained so as the labels of randomly masked both image and text tokens are predicted by unmasked tokens. In addition, to narrow the semantic gap between image and text in MIM, we propose Semantic MIM (SemMIM), in which the labels of masked image tokens are automatically given by a state-of-the-art human parser. Experimental results demonstrate that our BiLMa framework with SemMIM achieves state-of-the-art Rank@1 and mAP scores on three benchmarks.