Abstract: The invention provides a method for adapting a text-to-image (T2I) diffusion model for video editing by using spectral decomposition to achieve controlled spectral shifts in the model's weights. This adaptation involves maintaining constant singular vectors while selectively adjusting singular values in response to a text prompt. A spectral shift regularizer constrains adjustments, particularly limiting changes to larger singular values to ensure minimal deviation from the original model's structure. This approach allows efficient, prompt-driven video editing by modifying specific elements according to the prompt while preserving the original video context. By focusing on selective spectral adjustments, the method reduces adaptation time and computational demands, making it suitable for real-time and resource-sensitive applications, such as dynamic video editing for streaming services.

Abstract: The invention provides a method for adapting a text-to-image (T2I) diffusion model for video editing by using spectral decomposition to achieve controlled spectral shifts in the model's weights. This adaptation involves maintaining constant singular vectors while selectively adjusting singular values in response to a text prompt. A spectral shift regularizer constrains adjustments, particularly limiting changes to larger singular values to ensure minimal deviation from the original model's structure. This approach allows efficient, prompt-driven video editing by modifying specific elements according to the prompt while preserving the original video context. By focusing on selective spectral adjustments, the method reduces adaptation time and computational demands, making it suitable for real-time and resource-sensitive applications, such as dynamic video editing for streaming services.

Abstract: Described, herein, relates to a system of and method for digitally monitoring a large-scale dataset on a computing device and automatically detecting, in real-time, unknown class data in order to aid a machine learning model. Once machine learning models are deployed in the real-world applications, the models tend to encounter unknown-class (i.e., out-of-distribution) (hereinafter “OOD”) data during inference. Detecting out-of-distribution data is a crucial task in safety-critical applications to ensure safe deployment of deep learning models. It is desired that the machine learning model should only be confident about the type of data that has already seen in-distribution (hereinafter “ID”) class data which reinforces the driving principle of the OOD detection. The system and method may rely on contrastive feature learning of the largescale datasets, where the embeddings lie on a compact low-dimensional space.