# Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

arXiv: https://arxiv.org/abs/2502.08211 · HTML: https://mlsanity.com/paper/2502.08211/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [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)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt](https://mlsanity.com/paper/2206.07137/) (2022)
- [Dataset Inference: Ownership Resolution in Machine Learning](https://mlsanity.com/paper/2104.10706/) (2021)
- [Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training](https://mlsanity.com/paper/2301.02280/) (2023)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [T-MARS: Improving Visual Representations by Circumventing Text Feature Learning](https://mlsanity.com/paper/2307.03132/) (2023)
- [The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering](https://mlsanity.com/paper/2309.15954/) (2023)
- [HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts](https://mlsanity.com/paper/2404.17507/) (2024)
- [Multimodal Data Curation via Object Detection and Filter Ensembles](https://mlsanity.com/paper/2401.12225/) (2024)
- [Leveraging Image-Text Similarity and Caption Modification for the DataComp Challenge: Filtering Track and BYOD Track](https://mlsanity.com/paper/2310.14581/) (2023)

## Built on by (most cited first, 5 of 5)

- [Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining](https://mlsanity.com/paper/2503.08805/) (2025)
- [SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services](https://mlsanity.com/paper/2505.23065/) (2025)
- [Evian: Towards Explainable Visual Instruction-tuning Data Auditing](https://mlsanity.com/paper/2604.20544/) (2026)
- [Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach](https://mlsanity.com/paper/2510.23409/) (2025)
- [TADS: Task-Aware Data Selection for Multi-Task Multimodal Pre-Training](https://mlsanity.com/paper/2602.05251/) (2026)

## Abstract (arXiv)

In an era overwhelmed by vast amounts of data, the effective curation of web-crawl datasets is essential for optimizing model performance. This paper tackles the challenges associated with the unstructured and heterogeneous nature of such datasets. Traditional heuristic curation methods often inadequately capture complex features, resulting in biases and the exclusion of relevant data. We introduce an advanced, learning-driven approach, Ensemble Curation Of DAta ThroUgh Multimodal Operators (EcoDatum), incorporating a novel quality-guided deduplication method to ensure balanced feature distributions. EcoDatum strategically integrates various unimodal and multimodal data curation operators within a weak supervision ensemble framework, utilizing automated optimization to score each data point effectively. EcoDatum, which significantly improves the data curation quality and efficiency, outperforms existing state-of-the-art (SOTA) techniques, ranked 1st on the DataComp leaderboard, with an average performance score of 0.182 across 38 diverse evaluation datasets. This represents a 28% improvement over the DataComp baseline method, demonstrating its effectiveness in improving dataset curation and model training efficiency.