115 citations · 198 across the 5 of their papers we have counts for
7 papers
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations
Mohammadreza Zolfaghari, Yi Zhu, Peter Gehler +1
Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn c…
A Comprehensive Study of Deep Video Action Recognition
Yi Zhu, Xinyu Li, Chunhui Liu +7
Video action recognition is one of the representative tasks for video understanding. Over the last decade, we have witnessed great advancements in video action recognition thanks t…
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash +1
Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In th…
Temporal Shift GAN for Large Scale Video Generation
Andres Munoz, Mohammadreza Zolfaghari, Max Argus +1
Video generation models have become increasingly popular in the last few years, however the standard 2D architectures used today lack natural spatio-temporal modelling capabilities…
Learning Representations for Predicting Future Activities
Mohammadreza Zolfaghari, Özgün Çiçek, Syed Mohsin Ali +3
Foreseeing the future is one of the key factors of intelligence. It involves understanding of the past and current environment as well as decent experience of its possible dynamics…
ECO: Efficient Convolutional Network for Online Video Understanding
Mohammadreza Zolfaghari, Kamaljeet Singh, Thomas Brox
The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it misses important relationshi…