Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks. However, typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets ( i.e., direct transfer)

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research.