CLIP by Hand: 13-Step Walkthrough of Contrastive Language-Image Pre-training
ProfTomYeh · x · 2026-09-01
A simplified, hand-calculated walkthrough of OpenAI's CLIP (Contrastive Language-Image Pre-training) model, demonstrating how to map text and images into a shared embedding space in 13 steps.
Key steps include:
- Vectorization: Using word2vec for text and flattening image patches.
- Encoding & Projection: Passing vectors through encoders (Transformers) and a linear layer to project them into a shared 2D space.
- MatMul: Calculating the dot product of every text vector with every image vector.
- Softmax: Normalizing the results to create two similarity matrices.
- Loss Gradients: Using identity matrices as targets to derive gradients for optimization.
The takeaway: pairing a picture with a sentence essentially boils down to a single dot product in a shared space.
More from Research
- Numerai launches Signals v3.0 "Supernova": risk-awareness baked directly into the target — matlabulous · 2026-09-01
- COLMAP, the open-source 3D reconstruction tool, ships a new release — CSProfKGD · 2026-09-01
- ECCV 2026 Tutorial: Post-Training Alignment and Enhancement for Diffusion Models — RisingSayak · 2026-09-01
- PSI raises $58M seed to scale AI physics discovery — Scobleizer · 2026-09-01
- SWE-bench Multimodal Released by Stanford and Meta, Leaderboard Open — jyangballin · 2026-09-01
- SWE-bench Multimodal v2 Released: 480 Visual Coding Tasks — jyangballin · 2026-09-01