Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos.
Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM).
In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.
Ranked #1 on Low-Light Image Enhancement on LOL-v2-synthetic
Recently years have witnessed a rapid development of large language models (LLMs).
To address these challenges, we introduce a system that can jointly optimize distributed execution and gradient checkpointing plans.
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
Ranked #3 on Object Detection on COCO 2017 val
A holistic human dataset inevitably has insufficient and low-resolution information on local parts.