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Sea surface temperature (SST) forecasting depends on local temporal persistence, regional spatial dependence, and environmental conditions that evolve with the forecast date. We study how these heterogeneous conditions can be presented to a large language model (LLM) for regional multi-step forecasting without serializ...
Underwater object detection remains challenging in adverse aquatic environments, where severe image degradation caused by turbidity, light scattering, color attenuation, and low illumination substantially reduces detection reliability. Although real-world underwater datasets are essential, their limited scale and envir...
FM-ReID is proposed, an end-to-end framework that formulates local representation learning as selective competitive token routing as an effective way to augment holistic foundation-model representations, supporting competitive token routing as an effective way to augment holistic foundation-model representations.
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