RF-Sampling enhances flow matching models by implicitly performing gradient ascent on text-image alignment scores via linear textual combinations and flow inversion.
Learning transferable visual models from natural language supervision
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PPPQ-ANN is a hybrid FHE+TEE framework with product quantization that generates databases in under 2 hours and delivers over 50 QPS on million-scale datasets while preserving privacy.
The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.
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Reflective Flow Sampling Enhancement
RF-Sampling enhances flow matching models by implicitly performing gradient ascent on text-image alignment scores via linear textual combinations and flow inversion.
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Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment
PPPQ-ANN is a hybrid FHE+TEE framework with product quantization that generates databases in under 2 hours and delivers over 50 QPS on million-scale datasets while preserving privacy.
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Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.