Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models.
Results: The model showcased high accuracy in segmenting well-defined organs, achieving Dice Similarity Coefficient (DSC) scores of 0. 97 for the right and left lungs, and 0. 95 for the heart.
Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design.
To facilitate research in this new area, we build a richly annotated PSG-4D dataset consisting of 3K RGB-D videos with a total of 1M frames, each of which is labeled with 4D panoptic segmentation masks as well as fine-grained, dynamic scene graphs.
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades.
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.
However, this comes with high memory consumption, e. g., a well-trained Gaussian field may utilize three million Gaussian primitives and over 700 MB of memory.
Experimental results in classification and dense prediction tasks show that LeMeViT has a significant $1. 7 \times$ speedup, fewer parameters, and competitive performance compared to the baseline models, and achieves a better trade-off between efficiency and performance.
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals.
Instance segmentation is data-hungry, and as model capacity increases, data scale becomes crucial for improving the accuracy.
Ranked #7 on Object Detection on LVIS v1.0 val