Abstract: A system and method for automated extraction of contextual information from a set of data using an LMM is provided. The method begins by splitting the set of data into sub-data based on logical boundaries and converting each sub-data into an image. These images are then processed using a computer vision model and optical image recognition to extract metadata and positional coordinates. The images, extracted metadata, and positional coordinates are integrated into a custom prompt for the LMM. This prompt is processed to obtain relevant data points, which are subsequently validated for accuracy using a trained validation model that considers content density versus output records, regex pattern-based record matching scores, and template-based records approximation scores. An LLM is then used to normalize headers in the relevant data points. Finally, the method automatically extracts contextual information by generating responses to user queries on the normalized relevant data points using the LLM.

Abstract: Examples described herein include a graphics processing apparatus that includes a memory device and a display engine coupled to the memory device. The display engine is configured to apply dither on a region of an image and modify a first pixel of an image stored in the memory based on pseudo-random noise to reduce color banding impressions. In some examples, the pseudo-random noise is based on one or more of: local brightness estimation and one or more prior noise levels. In some examples, the display engine is to determine the local brightness estimation based on an average brightness of a pixel region surrounding the first pixel and the first pixel. In some examples, the display engine is configured to bound the pseudo-random noise based on a noise applied to a pixel in a same position as that of the first pixel in a prior frame within a same scene.

Abstract: Examples described herein include a graphics processing apparatus that includes a memory device and a display engine coupled to the memory device. The display engine is configured to apply dither on a region of an image and modify a first pixel of an image stored in the memory based on pseudo-random noise to reduce color banding impressions. In some examples, the pseudo-random noise is based on one or more of: local brightness estimation and one or more prior noise levels. In some examples, the display engine is to determine the local brightness estimation based on an average brightness of a pixel region surrounding the first pixel and the first pixel. In some examples, the display engine is configured to bound the pseudo-random noise based on a noise applied to a pixel in a same position as that of the first pixel in a prior frame within a same scene.