  Videos serve as a powerful medium to convey ideas, tell stories, and provide
detailed instructions, especially through long-format tutorials. Such tutorials
are valuable for learning new skills at one's own pace, yet they can be
overwhelming due to their length and dense content. Viewers often seek specific
information, like precise measurements or step-by-step execution details,
making it essential to extract and summarize key segments efficiently. An
intelligent, time-sensitive video assistant capable of summarizing and
detecting highlights in long videos is highly sought after. Recent advancements
in Multimodal Large Language Models offer promising solutions to develop such
an assistant. Our research explores the use of multimodal models to enhance
video summarization and step-by-step instruction generation within specific
domains. These models need to understand temporal events and relationships
among actions across video frames. Our approach focuses on fine-tuning TimeChat
to improve its performance in specific domains: cooking and medical procedures.
By training the model on domain-specific datasets like Tasty for cooking and
MedVidQA for medical procedures, we aim to enhance its ability to generate
concise, accurate summaries of instructional videos. We curate and restructure
these datasets to create high-quality video-centric instruction data. Our
findings indicate that when finetuned on domain-specific procedural data,
TimeChat can significantly improve the extraction and summarization of key
instructional steps in long-format videos. This research demonstrates the
potential of specialized multimodal models to assist with practical tasks by
providing personalized, step-by-step guidance tailored to the unique aspects of
each domain.
