# LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification

arXiv: https://arxiv.org/abs/2504.10174 · HTML: https://mlsanity.com/paper/2504.10174/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 40 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [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)
- [Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2007.09314/) (2020)
- [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)
- [CLIP-Driven Fine-grained Text-Image Person Re-identification](https://mlsanity.com/paper/2210.10276/) (2022)
- [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023)
- [Reparameterizable Subset Sampling via Continuous Relaxations](https://mlsanity.com/paper/1901.10517/) (2019)
- [Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search](https://mlsanity.com/paper/2101.03036/) (2021)
- [Learning Granularity-Unified Representations for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2207.07802/) (2022)
- [Person Re-identification Method Based on Color Attack and Joint Defence](https://mlsanity.com/paper/2111.09571/) (2021)
- [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023)
- [RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search](https://mlsanity.com/paper/2305.13653/) (2023)
- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [Cross-Modality Perturbation Synergy Attack for Person Re-identification](https://mlsanity.com/paper/2401.10090/) (2024)
- [Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID](https://mlsanity.com/paper/2405.04940/) (2024)
- [Multi-granularity Correspondence Learning from Long-term Noisy Videos](https://mlsanity.com/paper/2401.16702/) (2024)
- [UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity](https://mlsanity.com/paper/2312.03441/) (2023)
- [Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.06825/) (2024)
- [Chatting Makes Perfect: Chat-based Image Retrieval](https://mlsanity.com/paper/2305.20062/) (2023)
- [Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach](https://mlsanity.com/paper/2406.03411/) (2024)
- [Learning to Retrieve Videos by Asking Questions](https://mlsanity.com/paper/2205.05739/) (2022)
- [Simple Baselines for Interactive Video Retrieval with Questions and Answers](https://mlsanity.com/paper/2308.10402/) (2023)
- [WhittleSearch: Interactive Image Search with Relative Attribute Feedback](https://mlsanity.com/paper/1505.04141/) (2015)

## Built on by (most cited first, 9 of 9)

- [A Theory-Inspired Framework for Few-Shot Cross-Modal Sketch Person Re-Identification](https://mlsanity.com/paper/2511.18677/) (2025)
- [Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2505.00619/) (2025)
- [DialogueVPR: Towards Conversational Visual Place Recognition](https://mlsanity.com/paper/2607.14115/) (2026)
- [DSKC: Domain Style Modeling with Adaptive Knowledge Consolidation for Exemplar-free Lifelong Person Re-Identification](https://mlsanity.com/paper/2508.03516/) (2025)
- [FitPro: A Zero-Shot Framework for Interactive Text-based Pedestrian Retrieval in Open World](https://mlsanity.com/paper/2509.16674/) (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)
- [YesTrack: Referring Multi-Object Tracking via MLLM-based Yes/No Verification](https://mlsanity.com/paper/2609.02318/) (2026)
- [What Should We Ask Next? Retrieval-Aware Question Learning for Interactive ReID](https://mlsanity.com/paper/2609.21924/) (2026)

## Abstract (arXiv)

Traditional text-based person ReID assumes that person descriptions from witnesses are complete and provided at once. However, in real-world scenarios, such descriptions are often partial or vague. To address this limitation, we introduce a new task called interactive person re-identification (Inter-ReID). Inter-ReID is a dialogue-based retrieval task that iteratively refines initial descriptions through ongoing interactions with the witnesses. To facilitate the study of this new task, we construct a dialogue dataset that incorporates multiple types of questions by decomposing fine-grained attributes of individuals. We further propose LLaVA-ReID, a question model that generates targeted questions based on visual and textual contexts to elicit additional details about the target person. Leveraging a looking-forward strategy, we prioritize the most informative questions as supervision during training. Experimental results on both Inter-ReID and text-based ReID benchmarks demonstrate that LLaVA-ReID significantly outperforms baselines.