Introduction to Computer Vision

arXiv:2609.39627 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Accuracy — 58× in this paper · explained in 1 episode
• Calibration — 29× in this paper · explained in 7 episodes
• Optical Flow — 17× in this paper · explained in 2 episodes
• Adam — 14× in this paper · explained in 2 episodes
• Aperture — 14× in this paper · explained in 1 episode
• Clustering — 14× in this paper · explained in 2 episodes
• Instance Segmentation — 12× in this paper · explained in 3 episodes
• Core — 8× in this paper · explained in 2 episodes
• Transfer Learning — 8× in this paper · explained in 10 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Trace — 7× in this paper · explained in 2 episodes
• Quantization — 6× in this paper · explained in 12 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• alpha — 4× in this paper · explained in 1 episode
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Hysteresis — 3× in this paper · explained in 3 episodes
• Laplacian of Gaussian — 3× in this paper
• Loss Function — 3× in this paper · explained in 2 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Covariate Shift — 2× in this paper · explained in 2 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Wavelet Transform — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Difference of Gaussians — 1× in this paper
• Direct Linear Transform — 1× in this paper
• F1 Score — 1× in this paper · explained in 3 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

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