Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval
We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches embed both queries and items through a small encoder and retrieve the items lying closest to the query. While this approach allows efficient addition and retrieval of novel items, the small encoder lacks sufficient capacity for the necessary world knowledge in complex retrieval tasks. The extreme classification approaches have addressed this by learning a separate classifier for each item observed in the training set which significantly increases the representation capacity of the model. Such classifiers ou...
| Authors | Sachin Yadav, Deepak Saini, Anirudh Buvanesh et al. |
|---|---|
| Primary category | cs.IR |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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