activity
20162022
most citedIMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text

9 citations · 10 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2022

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…

cs.CL20221 cited

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…

cs.CV20229 cited

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…

cs.CV2021

Connecting What to Say With Where to Look by Modeling Human Attention Traces

Zihang Meng, Licheng Yu, Ning Zhang +4

We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [30], w…

cs.CL2021

SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

Satwik Kottur, Seungwhan Moon, Alborz Geramifard +1

Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal en…

cs.CL2016

NN-grams: Unifying neural network and n-gram language models for Speech Recognition

Babak Damavandi, Shankar Kumar, Noam Shazeer +1

We present NN-grams, a novel, hybrid language model integrating n-grams and neural networks (NN) for speech recognition. The model takes as input both word histories as well as n-g…