Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks.
Ranked #343 on Image Classification on ImageNet
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation.
By integrating our unsupervised pseudo masks into SA-1B's ground-truth masks and training UnSAM with only 1% of SA-1B, a lightly semi-supervised UnSAM can often segment entities overlooked by supervised SAM, exceeding SAM's AR by over 6. 7% and AP by 3. 9% on SA-1B.
Ranked #1 on Segmentation on SA-1B
This single-image calibration can benefit various downstream applications like image editing and 3D mapping.
Despite recent advances, existing VO methods still rely on heuristic design choices that require several weeks of hyperparameter tuning by human experts, hindering generalizability and robustness.
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge.
Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model.
By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10, 000 words while maintaining output quality.
Image Matching is a core component of all best-performing algorithms and pipelines in 3D vision.
Applying existing LLMs to legal systems without careful evaluation of their potential and limitations could pose significant risks in legal practice.