Talk: Takeaways from the Workshop on Event-Centric Video Retrieval, Oct 8

Takeaways from the SCALE 2024 Workshop on Event-Centric Video Retrieval

Reno Kriz, JHU HLTCOE

1:30-2:30 pm EDT Tuesday, October 8, 2024
ITE 325b, UMBC and online

Information dissemination for current events has traditionally consisted of professionally collected and produced materials, leading to large collections of well-written news articles and high-quality videos. As a result, most prior work in event analysis and retrieval has focused on leveraging this traditional news content, particularly in English. However, much of the event-centric content today is generated by non-professionals, such as on-the-scene witnesses to events who hastily capture videos and upload them to the internet without further editing; these are challenging to find due to quality variance, as well as a lack of text or speech overlays providing clear descriptions of what is occurring. To address this gap, SCALE 2024, a 10-week research workshop hosted at the Human Language Technology Center of Excellence (HLTCOE), focused on multilingual event-centric video retrieval, or the task of finding videos about specific current events. Around 50 researchers and students participated in this workshop and were split up into five sub-teams. The Infrastructure team focused on developing MultiVENT 2.0, a challenging new video retrieval dataset consisting of 20x more videos than prior work and targeted queries about specific world events across six languages. The other teams worked on improving models from specific modalities, specifically Vision, Optical Character Recognition (OCR), Audio, and Text. Overall, we came away with three primary findings: extracting specific text from a video allows us to take better advantage of powerful methods from the text information retrieval community; LLM summarization of initial text outputs from videos is helpful, especially for noisy text coming from OCR; and no one modality is sufficient, with fusing outputs from all modalities resulting in significantly higher performance.

Reno Kriz is a research scientist at the Johns Hopkins University Human Language Technology Center of Excellence (HLTCOE). His primary research interests involve leveraging large pre-trained models for a variety of natural language understanding tasks, including those crossing into other modalities, e.g., vision and speech understanding. These multimodal interests have recently involved the 2024 Summer Camp for Language Exploration (SCALE) on event-centric video retrieval and understanding. He received his PhD from the University of Pennsylvania, where he worked with Chris Callison-Burch and Marianna Apidianaki on text simplification and natural language generation. Prior to that, he received BA degrees in Computer Science, Mathematics, and Economics from Vassar College.

Part of the UMBC Language Technology Seminar Series