Between deadlines, bigger lessons slip through the cracks, buried in rejected papers, abandoned projects, or overlooked work. 

This workshop explores the "bitter lessons" of computer vision: hard-won wisdom our field has accumulated but rarely discusses. 

We'll examine Pyrrhic victories that won benchmarks but lost larger battles, "failed" ideas that were simply ahead of their time, concepts that keep resurfacing, and cases where simple methods unexpectedly triumphed. 

By openly reflecting on what worked, what didn't, and why, we aim to learn from our collective experience, reducing repeated mistakes and distilling insights to guide the next generation of research. 

A few examples would be:

• Lesson of Scale (Does Architecture Still Matter?)

• Lesson of Cyclicality (Why Do "Old" Ideas Keep Coming Back?)

• Lesson of Structure (When Does Elegance Beat Brute Force?)

• Lesson of Data (The Unreasonable Effectiveness of Uncuration?)

• Lesson of Metrology (Are We Chasing Benchmarks Off a Cliff?)

• Lesson of the Community (What is Academia's Role in the Age of Scale?)

Our previous editions:

• How to Stand Out in the Crowd (CVPR 2025)

• CV 20/20: A Retrospective Vision  (CVPR 2024) 

• Scholars & Big Models (CVPR 2023)

Other community building workshops: QVCV, Good Citizen of CVPR, Computer Vision After 5 Years, ML retrospectives