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Equation 6 · Part 1 · One Model, Many Modalities: What Multimodal Systems Actually Share

Symbol L

L=−1B∑i=1Blog⁡exp⁡(⟨ui,vi⟩/τ)∑j=1Bexp⁡(⟨ui,vj⟩/τ),\mathcal{L} = -\frac{1}{B} \sum_{i=1}^{B} \log \frac{\exp(\langle u_i, v_i \rangle / \tau)}{\sum_{j=1}^{B} \exp(\langle u_i, v_j \rangle / \tau)},
L\mathcal{L}

What this part means

In its softmax form the objective.

Its job in the formula

L is part of the quantity the equation computes from the expression on the right.

Where the article explains it

In its softmax form the objective is

The passage around this formula

Alignment is the step that made cross-modal retrieval work, and it has an unusually clean formulation. Contrastive language–image pretraining takes a batch of B image–text pairs, encodes each side separately into a normalised vector, and trains both encoders so that matched pairs score higher than mismatched ones. In its softmax form the objective is L=−1B∑i=1Blog⁡exp⁡(⟨ui,vi⟩/τ)∑j=1Bexp⁡(⟨ui,vj⟩/τ)\mathcal{L} = -\frac{1}{B} \sum_{i=1}^{B} \log \frac{\exp(\langle u_i, v_i \rangle / \tau)}{\sum_{j=1}^{B} \exp(\langle u_i, v_j \rangle / \tau)}. with image embedding uiu_i , text embedding viv_i , and a learned temperature τ\tau . Radford and colleagues showed that this simple pre-training task, applied to 400 million image–text pairs collected from the internet, matched the accuracy of the original ResNet-50 on ImageNet zero-shot without using any of the 1.28 million…

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Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

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Sources cited in the surrounding passage

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