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20172022
most citedVisual Features for Context-Aware Speech Recognition

38 citations · 137 across the 21 of their papers we have counts for

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7 papers · 1 filter

cs.CV2021

VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding

Hu Xu, Gargi Ghosh, Po-Yao Huang +5

We present a simplified, task-agnostic multi-modal pre-training approach that can accept either video or text input, or both for a variety of end tasks. Existing pre-training are t…

cs.CV20215 cited

Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models

Po-Yao Huang, Mandela Patrick, Junjie Hu +3

This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model tha…

cs.CV2021

Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning

Mandela Patrick, Yuki M. Asano, Bernie Huang +4

The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers h…

cs.CV2020

Audio-Visual Event Recognition through the lens of Adversary

Juncheng B Li, Kaixin Ma, Shuhui Qu +2

As audio/visual classification models are widely deployed for sensitive tasks like content filtering at scale, it is critical to understand their robustness along with improving th…

cs.CV2020

Support-set bottlenecks for video-text representation learning

Mandela Patrick, Po-Yao Huang, Yuki Asano +4

The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to…

cs.CV2020

How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language

Amanda Duarte, Shruti Palaskar, Lucas Ventura +5

One of the factors that have hindered progress in the areas of sign language recognition, translation, and production is the absence of large annotated datasets. Towards this end,…