mlsanityData to 7 Oct 2026

Understanding Bias in Large-Scale Visual Datasets

arXiv 2412.01876 ↗First posted 2 Dec 2024cs.CV cs.LGcited by 15

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Abstract (arXiv)

A recent study has shown that large-scale visual datasets are very biased: they can be easily classified by modern neural networks. However, the concrete forms of bias among these datasets remain unclear. In this study, we propose a framework to identify the unique visual attributes distinguishing these datasets. Our approach applies various transformations to extract semantic, structural, boundary, color, and frequency information from datasets, and assess how much each type of information reflects their bias. We further decompose their semantic bias with object-level analysis, and leverage natural language methods to generate detailed, open-ended descriptions of each dataset's characteristics. Our work aims to help researchers understand the bias in existing large-scale pre-training datasets, and build more diverse and representative ones in the future. Our project page and code are available at http://boyazeng.github.io/understand_bias .

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 61 of its 81 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

61 of 81 references.

References · 61

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Citing papers per year: 2024: 2; 2025: 9; 2026: 4.

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  1. 2025Idiosyncrasies in Large Language Modelscited by 32
  2. 2025Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weightscited by 18
  3. 2025Alignment Revisited: Are Large Language Models Consistent in Stated and Revealed Preferences?cited by 7
  4. 2025PRISM: Reducing Spurious Implicit Biases in Vision-Language Models with LLM-Guided Embedding Projectioncited by 5
  5. 2025Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluationcited by 5
  6. 2025ConceptScope: Characterizing Dataset Bias via Disentangled Visual Conceptscited by 5
  7. 2024Measuring Fingerprints of Web-filtered Text Datasets and Fingerprint Propagation Through Trainingcited by 4
  8. 2024Low-Biased General Annotated Dataset Generationcited by 1
  9. 2025Understanding Dataset Bias in Medical Imaging: A Case Study on Chest X-rayscited by 1
  10. 2026Guess the Unified Model: How Much Can We Recover from Generated Images?cited by 1
  11. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  12. 2025SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Taskscited by 0
3 more
  1. 2026Coverage-aware Semantic Representation Learning for Underrepresented Visual Conceptscited by 0
  2. 2026What Are We Really Measuring? Rethinking Dataset Bias in Web-Scale Natural Image Collections via Unsupervised Semantic Clusteringcited by 0
  3. 2026Visual General Intelligence: A White Papercited by 0