Search Results for author: Andrea Burns

Found 8 papers, 5 papers with code

Language-Guided Audio-Visual Source Separation via Trimodal Consistency

no code implementations CVPR 2023 Reuben Tan, Arijit Ray, Andrea Burns, Bryan A. Plummer, Justin Salamon, Oriol Nieto, Bryan Russell, Kate Saenko

We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data.

Audio Source Separation Natural Language Queries

A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility

1 code implementation4 Feb 2022 Andrea Burns, Deniz Arsan, Sanjna Agrawal, Ranjitha Kumar, Kate Saenko, Bryan A. Plummer

To study VLN with unknown command feasibility, we introduce a new dataset Mobile app Tasks with Iterative Feedback (MoTIF), where the goal is to complete a natural language command in a mobile app.

Common Sense Reasoning Question Answering +1

Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions

1 code implementation14 Aug 2021 Andrea Burns, Aaron Sarna, Dilip Krishnan, Aaron Maschinot

Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs).

Contrastive Learning Disentanglement

Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments

1 code implementation17 Apr 2021 Andrea Burns, Deniz Arsan, Sanjna Agrawal, Ranjitha Kumar, Kate Saenko, Bryan A. Plummer

In recent years, vision-language research has shifted to study tasks which require more complex reasoning, such as interactive question answering, visual common sense reasoning, and question-answer plausibility prediction.

Common Sense Reasoning Question Answering

Learning to Scale Multilingual Representations for Vision-Language Tasks

no code implementations ECCV 2020 Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, Bryan A. Plummer

Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added.

Language Modelling Machine Translation +3

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