GeoQuery is a zero-shot retrieval system that optimizes text prompts on a proxy subset so language embeddings correlate with frozen CLAY visual embeddings, then performs text search followed by visual nearest-neighbor lookup, reaching 31.6% accuracy within 50 km on 76 disaster queries.
Sigmoid loss for language image pre-training
3 Pith papers cite this work. Polarity classification is still indexing.
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Quantum kernels in QSVM deliver higher minority-class F1 scores than classical linear or RBF kernels on medical foundation model embeddings for binary insurance classification, avoiding classical collapse in noiseless simulation.
Using lexical concreteness to guide contrastive negative mining and a new margin-based Cement loss, the Slipform framework reaches state-of-the-art on compositional benchmarks for vision-language models.
citing papers explorer
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Zero-Shot Satellite Image Retrieval through Joint Embeddings: Application to Crisis Response
GeoQuery is a zero-shot retrieval system that optimizes text prompts on a proxy subset so language embeddings correlate with frozen CLAY visual embeddings, then performs text search followed by visual nearest-neighbor lookup, reaching 31.6% accuracy within 50 km on 76 disaster queries.
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Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
Quantum kernels in QSVM deliver higher minority-class F1 scores than classical linear or RBF kernels on medical foundation model embeddings for binary insurance classification, avoiding classical collapse in noiseless simulation.
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Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding
Using lexical concreteness to guide contrastive negative mining and a new margin-based Cement loss, the Slipform framework reaches state-of-the-art on compositional benchmarks for vision-language models.