Memento 4.0: A Prototype Conversational Search System for LSC'24

The practice of lifelogging, capturing one's daily experiences through wearable devices, has evolved significantly over the last decade, presenting both challenges and opportunities in information retrieval. This paper presents an early prototype of a conversational lifelog retrieval system designed to address the open challenges in this domain. Our system integrates a hierarchical event segmentation approach to automatically organize lifelog data into meaningful events, facilitating event-based retrieval over traditional image retrieval. Moreover, we incorporate a question-answering pipeline, leveraging large language models such as GPT-3.5 Turbo and Mistral7B, to enable free-form natural language interaction with the lifelog dataset. Moreover, we enhance our system's user interface by building on previous versions to streamline event-based retrieval and question-answering functionalities.