Concept-centric short captions and cross-modal attention pooling yield SOTA compositionality in contrastive V&L models without degrading zero-shot or retrieval performance.
spaCy 2: Natural lan- guage understanding with Bloom embeddings, convolutional neural networks and incremental parsing
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No Hard Negatives Required: Concept Centric Learning Leads to Compositionality without Degrading Zero-shot Capabilities of Contrastive Models
Concept-centric short captions and cross-modal attention pooling yield SOTA compositionality in contrastive V&L models without degrading zero-shot or retrieval performance.