This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation.
To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions.
This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i. e. a minimap).
Many settings in machine learning require the selection of a rotation representation.
Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization.
Given that such models can classify, delineate, and localize objects in 2D, we ask whether they also represent their 3D structure?
We study the use of large language model-based agents for interacting with software via web browsers.
Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions.
The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
Ranked #1 on Time Series Forecasting on Weather (96)
Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy.