OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering

People often capture memories through photos, screenshots, and videos. While existing AI-based tools enable querying this data using natural language, they mostly only support retrieving individual pieces of information like certain objects in photos and struggle with answering more complex queries that involve interpreting interconnected memories like event sequences. We conducted a one-month diary study to collect realistic user queries and generated a taxonomy of necessary contextual information for integrating with captured memories. We then introduce OmniQuery, a novel system that is able to answer complex personal memory-related questions that require extracting and inferring contextual information. OmniQuery augments single captured memories through integrating scattered contextual information from multiple interconnected memories, retrieves relevant memories, and uses a large language model (LLM) to comprehensive answers. In human evaluations, we show the effectiveness of OmniQuery with an accuracy of 71.5%, and it outperformed a conventional RAG system, winning or tying in 74.5% of the time.

Automated Conversion of Music Videos into Lyric Videos

Musicians and fans often produce lyric videos, a form of music videos that showcase the song's lyrics, for their favorite songs. However, making such videos can be challenging and time-consuming as the lyrics need to be added in synchrony and visual harmony with the video. Informed by prior work and close examination of existing lyric videos, we propose a set of design guidelines to help creators make such videos. Our guidelines ensure the readability of the lyric text while maintaining a unified focus of attention. We instantiate these guidelines in a fully automated pipeline that converts an input music video into a lyric video. We demonstrate the robustness of our pipeline by generating lyric videos from a diverse range of input sources. A user study shows that lyric videos generated by our pipeline are effective in maintaining text readability and unifying the focus of attention.

Megapixel Photon-Counting Color Imaging using Quanta Image Sensor

Quanta Image Sensor (QIS) is a single-photon detector designed for extremely low light imaging conditions. Majority of the existing QIS prototypes are monochrome based on single-photon avalanche diodes (SPAD). Color imaging has not been demonstrated with single-photon detectors due to the intrinsic difficulty of shrinking the pixel size and increasing the spatial resolution while maintaining acceptable intra-pixel cross-talk. In this paper, we present image reconstruction of the first color QIS with a resolution of $1024 \times 1024$ pixels, supporting both single-bit and multi-bit photon counting capability. Our color image reconstruction is enabled by a customized joint demosaicing-denoising algorithm, leveraging truncated Poisson statistics and variance stabilizing transforms. Experimental results of the new sensor and algorithm demonstrate superior color imaging performance for very low-light conditions with a mean exposure of as low as a few photons per pixel.