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Radford and colleagues’ 2021 paper, “Learning Transferable Visual Models From Natural Language Supervision,” is usually remembered simply as CLIP, and the compression loses the specific thing that made it a turning point rather than an incremental improvement. The method itself is not exotic: encode an image, encode its paired caption, and train both encoders so that the true pairing scores higher than every mismatched pairing drawn from the same batch. For a batch of B image-text pairs with normalised image embedding $u_i$ , text embedding $v_i$ , and a learned temperature $\tau$ , the symmetric form of the objective is $\mathcal{L} = -\frac{1}{2B}\sum_{i=1}^{B}\left[\log\frac{\exp(\langle u_i, v_i\rangle/\tau)}{\sum_{j=1}^{B}\exp(\langle u_i, v_j\rangle/\tau)} + \log\frac{\exp(\langle u_i, v_i\rangle/\tau)}{\sum_{j=1}^{B}\exp(\langle u_j, v_i\rangle/\tau)}\right]$. an image-to-text and a text-to-image cross-entropy…

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