Dynamics forecasting refers to the task of predicting the future behavior of a dynamic system, involving learning the underlying dynamics governing the system's evolution to make accurate predictions

M-7B significantly improves the performance on real-world long-context tasks (e.g., 23.5

Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing. A specialized area within generative modeling is layout-to-image (L2I) generation, where predefined layouts of objects guide the generative process. In this study, we introduce a novel regional cross-attention module tailored to enrich layout-to-image generation. This module notably improves the representation of layout regions, particularly in scenarios where existing methods struggle with highly complex and detailed textual descriptions. Moreover, while current open-vocabulary L2I methods are trained in an open-set setting, their evaluations often occur in closed-set environments. To bridge this gap, we propose two metrics to assess L2I performance in open-vocabulary scenarios.

The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications.

However, compared with the huge success of generative models in natural language processing [Tou-vron et al., 2023] and computer vision [Rombach et al., 2021], graph generation is faced with many

This work takes one step towards data encryption by incorporating some crucial ingredients of learning algorithm.

Computational intensiveness of deep learning has motivated low-precision arithmetic designs.