cv-pipeline-eliminated-by-end-to-end-learning

OUT derived (depth 2)

Created 2026-06-21T11:39:46+00:00

Deep learning would eliminate computer vision's complex six-stage pipeline (acquisition through decision) by learning end-to-end representations that subsume hand-engineered preprocessing, feature extraction, and segmentation stages.

Justifications

SL — Superhuman vision performance suggests pipeline obsolescence, but feature engineering persists even with deep learning

Antecedents (all must be IN):

IN cv-system-pipeline-six-stages — A computer vision system pipeline has 6 stages: image acquisition, pre-processing, feature extraction, detection/segmentation, high-level processing, and decision making
IN deep-learning-superhuman-vision-from-alexnet — AlexNet's 2012 ImageNet victory is widely considered the start of the deep learning revolution in computer vision. Following this, deep learning surpassed prior computer vision methods on benchmarks for classification, segmentation, and optical flow, and exceeded human-level performance on specific visual recognition tasks such as traffic sign recognition (2011) and face recognition (2014).

Unless (any of these IN defeats this justification):

IN deep-learning-reduces-but-does-not-eliminate-feature-engineering — Deep learning reduces but does not eliminate the need for feature engineering — representation learning automates some feature construction but manual engineering remains valuable for structured and tabular data.