Search Results for author:
Found 6 papers, 3 papers with code
VEATIC: Video-based Emotion and Affect Tracking in Context Dataset
Along with the dataset, we propose a new computer vision task to infer the affect of the selected character via both context and character information in each video frame.
Situated and Interactive Multimodal Conversations
Next generation virtual assistants are envisioned to handle multimodal inputs (e. g., vision, memories of previous interactions, in addition to the user's utterances), and perform multimodal actions (e. g., displaying a route in addition to generating the system's utterance).
SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform
To this end, we introduce SIMMC, an extension to ParlAI for multi-modal conversational data collection and system evaluation.
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos.
Learning Robust Dialog Policies in Noisy Environments
In this paper, we focus on learning robust dialog policies to recover from these errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Predicting Driver Attention in Critical Situations
Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol---tracking eye movements during driving.
