2 citations · 3 across the 3 of their papers we have counts for
4 papers
Progressive Data Dropout: An Embarrassingly Simple Approach to Faster Training
Shriram M Sathiyanarayanan, Xinyue Hao, Shihao Hou +4
The success of the machine learning field has reliably depended on training on large datasets. While effective, this trend comes at an extraordinary cost. This is due to two deeply…
Principles of Visual Tokens for Efficient Video Understanding
Xinyue Hao, Gen Li, Shreyank N Gowda +4
Video understanding has made huge strides in recent years, relying largely on the power of transformers. As this architecture is notoriously expensive and video data is highly redu…
Optimizing ViViT Training: Time and Memory Reduction for Action Recognition
Shreyank N Gowda, Anurag Arnab, Jonathan Huang
In this paper, we address the challenges posed by the substantial training time and memory consumption associated with video transformers, focusing on the ViViT (Video Vision Trans…
Compressive Visual Representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama +2
Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. Rec…