196 citations · 378 across the 23 of their papers we have counts for
6 papers · 1 filter
Evolving Space-Time Neural Architectures for Videos
AJ Piergiovanni, Anelia Angelova, Alexander Toshev +1
We present a new method for finding video CNN architectures that capture rich spatio-temporal information in videos. Previous work, taking advantage of 3D convolutions, obtained pr…
Representation Flow for Action Recognition
AJ Piergiovanni, Michael S. Ryoo
In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer d…
Learning Multimodal Representations for Unseen Activities
AJ Piergiovanni, Michael S. Ryoo
We present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. We first compare the effect of placing various constra…
Learning Real-World Robot Policies by Dreaming
AJ Piergiovanni, Alan Wu, Michael S. Ryoo
Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining milli…
Fine-grained Activity Recognition in Baseball Videos
AJ Piergiovanni, Michael S. Ryoo
In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification…
Temporal Gaussian Mixture Layer for Videos
AJ Piergiovanni, Michael S. Ryoo
We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in con…