Position: Unlabeled IS NOT Equal to No Human Supervision in Visual Learning

2026-09-02

AI Summary

本文讨论了无标签数据不等于无人监督的问题，提出应明确识别不同来源的监督，并建议研究者披露数据选择和学习目标中的先验知识。

Abstract

This position paper argues that the absence of labels does not imply the absence of human supervision in visual learning, and urges the research community to identify sources of supervision more explicitly. Many recent methods in computer vision build upon representations learned from large-scale unlabeled data, and are therefore grouped under the same umbrella term ``unsupervised.'' However, different data curation schemes and training objectives embed substantially different human priors on which models rely, and we argue that one ``unsupervised'' umbrella term is no longer capturing these distinctions. This ambiguity makes it harder to compare unsupervised learning research conducted under different assumptions, coinciding with a sharp decline in papers titled with ``unsupervised'' in flagship computer vision conferences since 2021, despite continued growth of the field. While we fully embrace pre-training as a strong foundation for modern computer vision, we advocate for a community-level effort toward greater conceptual clarity: authors are encouraged to disclose priors in data selection and learning objectives, and to specify which components of a learning pipeline depend on which assumptions. Standardized disclosure practices can improve academic communication, ensure fairer comparisons, and preserve methodological diversity in unsupervised learning.

Problem

• Unlabeled Data
• Human Supervision
• Visual Learning
• Unsupervised Learning

Innovation

• Unlabeled Data
• Human Supervision
• Conceptual Clarity
• Data Curation
• Standardized Disclosure