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This work evaluates 21 representative models, including large language models, vision-language models, time-series language models, and physiological signal foundation models under three complementary settings, and shows that none of the evaluated models achieves consistently strong performance across physiological sig...
This work proposes an enhanced 3D segmentation framework, UAtten-Unetr, designed to improve segmentation accuracy and robustness in complex medical scenarios, and innovatively developed a unified loss function based on bimodal modality-specific Dice constraints and uncertainty regularization, optimized for synchronous...
Experiments on diverse video datasets show that CoANeRV consistently improves reconstruction quality over prior feed-forward NeRV and INR baselines, reduces peak memory compared with attention-based coordinate decoders, and provides efficient amortized encoding without per-video optimization.
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