Zero-shot image classification, by definition, requires classifying images into categories the model was never explicitly trained to recognize, with no task-specific labeled examples. CLIP-style models enable this by encoding both images and candidate text labels (e.g., "a photo of a {class}") into a shared embedding space; classification then reduces to a similarity comparison — computing cosine similarity between the image embedding and each candidate text embedding and selecting the closest match. This is the crucial architectural step: without a shared embedding space linking visual and textual semantics, there is no mechanism to generalize to unseen classes using only their names or descriptions.

Option B directly contradicts the "zero-shot" premise — manual labeling of the target dataset is precisely what zero-shot classification is designed to avoid; if labels were being collected for the target classes, the task would be standard supervised classification, not zero-shot. Option A (image enhancement) may marginally help downstream accuracy but is not the crucial, defining step. Option D is incoherent with how CLIP-style zero-shot classification actually works — the textual description of each candidate class is the essential input that makes zero-shot generalization possible; eliminating it would remove the mechanism entirely, not improve it.