A Meta AutoEncoder framework enables adaptive, progressive compression of visual features for low-latency edge-cloud VLM inference without model fine-tuning.
Perceptual losses for real-time style transfer and super-resolution,
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FAS-UCM generates user-specific spoof images via style transfer to train a CNN that distinguishes live and spoof faces with 0.22 average error rate on the SiW database.
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Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
A Meta AutoEncoder framework enables adaptive, progressive compression of visual features for low-latency edge-cloud VLM inference without model fine-tuning.
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Style Transfer Applied to Face Liveness Detection with User-Centered Models
FAS-UCM generates user-specific spoof images via style transfer to train a CNN that distinguishes live and spoof faces with 0.22 average error rate on the SiW database.