# INQUIRE: A Natural World Text-to-Image Retrieval Benchmark

arXiv: https://arxiv.org/abs/2411.02537 · HTML: https://mlsanity.com/paper/2411.02537/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 54 of its 79 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 30 of 54)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)

## Built on by (most cited first, 30 of 33)

- [BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning](https://mlsanity.com/paper/2505.23883/) (2025)
- [ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation](https://mlsanity.com/paper/2502.09411/) (2025)
- [Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries](https://mlsanity.com/paper/2502.16636/) (2025)
- [HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents](https://mlsanity.com/paper/2605.07177/) (2026)
- [ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval](https://mlsanity.com/paper/2502.15682/) (2025)
- [WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization](https://mlsanity.com/paper/2508.09560/) (2025)
- [MR2-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval](https://mlsanity.com/paper/2509.26378/) (2025)
- [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://mlsanity.com/paper/2512.23592/) (2025)
- [Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring](https://mlsanity.com/paper/2503.01691/) (2025)
- [VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval](https://mlsanity.com/paper/2505.20291/) (2025)
- [Global and Local Entailment Learning for Natural World Imagery](https://mlsanity.com/paper/2506.21476/) (2025)
- [BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models](https://mlsanity.com/paper/2510.20095/) (2025)
- [MUVR: A Multi-Modal Untrimmed Video Retrieval Benchmark with Multi-Level Visual Correspondence](https://mlsanity.com/paper/2510.21406/) (2025)
- [ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge](https://mlsanity.com/paper/2602.09839/) (2026)
- [QuARI: Query Adaptive Retrieval Improvement](https://mlsanity.com/paper/2505.21647/) (2025)
- [Robustness in Both Domains: CLIP Needs a Robust Text Encoder](https://mlsanity.com/paper/2506.03355/) (2025)
- [Autonomous Scientific Discovery via Iterative Meta-Reflection](https://mlsanity.com/paper/2607.01131/) (2026)
- [AnimalClue: Recognizing Animals by their Traces](https://mlsanity.com/paper/2507.20240/) (2025)
- [ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology](https://mlsanity.com/paper/2511.02946/) (2025)
- [Semantic search for 100M+ galaxy images using AI-generated captions](https://mlsanity.com/paper/2512.11982/) (2025)
- [Few Shots Text to Image Retrieval: New Benchmarking Dataset and Optimization Methods](https://mlsanity.com/paper/2603.25891/) (2026)
- [Energy-Efficient Plant Monitoring via Knowledge Distillation](https://mlsanity.com/paper/2604.27178/) (2026)
- [Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data](https://mlsanity.com/paper/2607.09876/) (2026)
- [INQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases](https://mlsanity.com/paper/2511.15656/) (2025)
- [Compact Hypercube Embeddings for Fast Text-based Wildlife Observation Retrieval](https://mlsanity.com/paper/2601.22783/) (2026)
- [Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation](https://mlsanity.com/paper/2602.00681/) (2026)
- [Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach](https://mlsanity.com/paper/2603.06660/) (2026)
- [eLasmobranc Dataset: An Image Dataset for Elasmobranch Species Recognition and Biodiversity Monitoring](https://mlsanity.com/paper/2603.10724/) (2026)
- [BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment](https://mlsanity.com/paper/2603.23883/) (2026)
- [RealBirdID: Benchmarking Bird Species Identification in the Era of MLLMs](https://mlsanity.com/paper/2603.27033/) (2026)

## Abstract (arXiv)

We introduce INQUIRE, a text-to-image retrieval benchmark designed to challenge multimodal vision-language models on expert-level queries. INQUIRE includes iNaturalist 2024 (iNat24), a new dataset of five million natural world images, along with 250 expert-level retrieval queries. These queries are paired with all relevant images comprehensively labeled within iNat24, comprising 33,000 total matches. Queries span categories such as species identification, context, behavior, and appearance, emphasizing tasks that require nuanced image understanding and domain expertise. Our benchmark evaluates two core retrieval tasks: (1) INQUIRE-Fullrank, a full dataset ranking task, and (2) INQUIRE-Rerank, a reranking task for refining top-100 retrievals. Detailed evaluation of a range of recent multimodal models demonstrates that INQUIRE poses a significant challenge, with the best models failing to achieve an mAP@50 above 50%. In addition, we show that reranking with more powerful multimodal models can enhance retrieval performance, yet there remains a significant margin for improvement. By focusing on scientifically-motivated ecological challenges, INQUIRE aims to bridge the gap between AI capabilities and the needs of real-world scientific inquiry, encouraging the development of retrieval systems that can assist with accelerating ecological and biodiversity research. Our dataset and code are available at https://inquire-benchmark.github.io