In this work, we present our system Memento 3.0 for participation
in the Lifelog Search Challenge 2023, which is a successor to the
previous 2 iterations of our system called Memento 1.0 and
Memento 2.0. Memento 3.0 employs image-text embeddings derived from OpenAI CLIP models as well as larger OpenCLIP models
trained on ∼5x more data. Our system also significantly reduces
the query processing time by almost 75% when compared to its
predecessor systems by employing a cluster-based search technique.
We additionally make important updates to the system’s user interface to offer more flexibility to the user and at the same time be
better suited to efficiently handle new query types introduced in
the Lifelog Search Challenge.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Workshop
Refereed:
Yes
Uncontrolled Keywords:
Information systems; Retrieval models and ranking; Search interfaces.