activity
20122021
most citedLearning to Ground Multi-Agent Communication with Autoencoders

16 citations · 18 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG202116 cited

Learning to Ground Multi-Agent Communication with Autoencoders

Toru Lin, Minyoung Huh, Chris Stauffer +2

Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process, but it may require many generations of trial a…

cs.CV2021

Cross-Modal Retrieval Augmentation for Multi-Modal Classification

Shir Gur, Natalia Neverova, Chris Stauffer +3

Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here,…

cs.CV20172 cited

End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering

SouYoung Jin, Hang Su, Chris Stauffer +1

We present an end-to-end system for detecting and clustering faces by identity in full-length movies. Unlike works that start with a predefined set of detected faces, we consider t…

cs.CV2016

Template Adaptation for Face Verification and Identification

Nate Crosswhite, Jeffrey Byrne, Omkar M. Parkhi +3

Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces…

cs.LG2012

Factored Latent Analysis for far-field tracking data

Chris Stauffer

This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent cla…