81 citations · 171 across the 6 of their papers we have counts for
7 papers
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
Mandela Patrick, Dylan Campbell, Yuki M. Asano +5
In video transformers, the time dimension is often treated in the same way as the two spatial dimensions. However, in a scene where objects or the camera may move, a physical point…
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…
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…
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…
Labelling unlabelled videos from scratch with multi-modal self-supervision
Yuki M. Asano, Mandela Patrick, Christian Rupprecht +1
A large part of the current success of deep learning lies in the effectiveness of data -- more precisely: labelled data. Yet, labelling a dataset with human annotation continues to…
On Compositions of Transformations in Contrastive Self-Supervised Learning
Mandela Patrick, Yuki M. Asano, Polina Kuznetsova +4
In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning. In this paper, we general…