4 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Walk and Read Less: Improving the Efficiency of Vision-and-Language Navigation via Tuning-Free Multimodal Token Pruning
Wenda Qin, Andrea Burns, Bryan A. Plummer +1
Large models achieve strong performance on Vision-and-Language Navigation (VLN) tasks, but are costly to run in resource-limited environments. Token pruning offers appealing tradeo…
Tell Me What's Next: Textual Foresight for Generic UI Representations
Andrea Burns, Kate Saenko, Bryan A. Plummer
Mobile app user interfaces (UIs) are rich with action, text, structure, and image content that can be utilized to learn generic UI representations for tasks like automating user co…
Language-Guided Audio-Visual Source Separation via Trimodal Consistency
Reuben Tan, Arijit Ray, Andrea Burns +5
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 tra…
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
Andrea Burns, Aaron Sarna, Dilip Krishnan +1
Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs). While prior work has focused on generative methods for…
Learning to Scale Multilingual Representations for Vision-Language Tasks
Andrea Burns, Donghyun Kim, Derry Wijaya +2
Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are a…
Language Features Matter: Effective Language Representations for Vision-Language Tasks
Andrea Burns, Reuben Tan, Kate Saenko +2
Shouldn't language and vision features be treated equally in vision-language (VL) tasks? Many VL approaches treat the language component as an afterthought, using simple language m…