83 citations · 250 across the 21 of their papers we have counts for
6 papers · 1 filter
Normalized Contrastive Learning for Text-Video Retrieval
Yookoon Park, Mahmoud Azab, Bo Xiong +4
Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness. In this work, however, we reveal that cross-modal contra…
Navigating Connected Memories with a Task-oriented Dialog System
Seungwhan Moon, Satwik Kottur, Alborz Geramifard +1
Recent years have seen an increasing trend in the volume of personal media captured by users, thanks to the advent of smartphones and smart glasses, resulting in large media collec…
Tell Your Story: Task-Oriented Dialogs for Interactive Content Creation
Satwik Kottur, Seungwhan Moon, Aram H. Markosyan +3
People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due…
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin +4
We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with video and text, by projecting them into the joint represen…
Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks
Pedro Rodriguez, Mahmoud Azab, Becka Silvert +4
Searching troves of videos with textual descriptions is a core multimodal retrieval task. Owing to the lack of a purpose-built dataset for text-to-video retrieval, video captioning…
KETOD: Knowledge-Enriched Task-Oriented Dialogue
Zhiyu Chen, Bing Liu, Seungwhan Moon +3
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse natura…