# Fantastic Data and How to Query Them

arXiv: https://arxiv.org/abs/2201.05026 · HTML: https://mlsanity.com/paper/2201.05026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 23 of 23)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space](https://mlsanity.com/paper/2105.01879/) (2021)
- [Object Detection with a Unified Label Space from Multiple Datasets](https://mlsanity.com/paper/2008.06614/) (2020)
- [Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking](https://mlsanity.com/paper/2106.06052/) (2021)
- [Relation-aware Instance Refinement for Weakly Supervised Visual Grounding](https://mlsanity.com/paper/2103.12989/) (2021)
- [Panoptic Narrative Grounding](https://mlsanity.com/paper/2109.04988/) (2021)
- [Overton: A Data System for Monitoring and Improving Machine-Learned Products](https://mlsanity.com/paper/1909.05372/) (2019)
- [Taskology: Utilizing Task Relations at Scale](https://mlsanity.com/paper/2005.07289/) (2020)

## Built on by (most cited first, 1 of 1)

- [VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph](https://mlsanity.com/paper/2309.13610/) (2023)

## Abstract (arXiv)

It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Learning models but also limits the understanding of the data, which is very important for data-centric AI. In this paper, we present our vision about a unified framework for different datasets so that they can be integrated and queried easily, e.g., using standard query languages. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios. Our demonstration is available at https://vision.semkg.org.