By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on Question Answering on CoQA (Overall metric)
However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multidomain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models.
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity.
Through extensive analysis of SfM initialization in the frequency domain and analysis of a 1D regression task with multiple 1D Gaussians, we propose a novel optimization strategy dubbed RAIN-GS (Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting), that successfully trains 3D Gaussians from random point clouds.
Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP) applications.
Given the combinatorial complexity, and thus computation time, of experimenting with hand-tuning prompts for large black-box models, we then compared the performance of the best "positive thinking" prompt against the output of systematic prompt optimization.
Ranked #103 on Arithmetic Reasoning on GSM8K
Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera.
Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis.
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.
In our work, we propose GeMap ($\textbf{Ge}$ometry $\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map instances beyond basic perception.