Search Results for author: Mandela Patrick

Found 8 papers, 6 papers with code

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

1 code implementation ICCV 2021 Mandela Patrick, Yuki M. Asano, Bernie Huang, Ishan Misra, Florian Metze, Joao Henriques, Andrea Vedaldi

First, for space, we show that spatial augmentations such as cropping do work well for videos too, but that previous implementations, due to the high processing and memory cost, could not do this at a scale sufficient for it to work well.

Representation Learning Self-Supervised Learning

Support-set bottlenecks for video-text representation learning

no code implementations ICLR 2021 Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander Hauptmann, João Henriques, Andrea Vedaldi

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 be related, such as text and video from the same sample, and pushes away the representations of all other pairs.

Contrastive Learning Representation Learning +1

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

1 code implementation ICCV 2019 Ruth Fong, Mandela Patrick, Andrea Vedaldi

In this paper, we discuss some of the shortcomings of existing approaches to perturbation analysis and address them by introducing the concept of extremal perturbations, which are theoretically grounded and interpretable.

Interpretable Machine Learning

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