We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP (Contrastive Language–Image Pre-training) can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GPT-2 and 3.
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Although deep learning has revolutionized computer vision, current approaches have several major problems: typical vision datasets are labor intensive and costly to create while teaching only a narrow set of visual concepts; standard vision models are good at one task and one task only, and require significant …
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