7 citations · 18 across the 4 of their papers we have counts for
10 papers
AssembleNet++: Assembling Modality Representations via Attention Connections
Michael S. Ryoo, AJ Piergiovanni, Juhana Kangaspunta +1
We create a family of powerful video models which are able to: (i) learn interactions between semantic object information and raw appearance and motion features, and (ii) deploy at…
Adversarial Generative Grammars for Human Activity Prediction
AJ Piergiovanni, Anelia Angelova, Alexander Toshev +1
In this paper we propose an adversarial generative grammar model for future prediction. The objective is to learn a model that explicitly captures temporal dependencies, providing…
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann +5
Convolutional operations have two limitations: (1) do not explicitly model where to focus as the same filter is applied to all the positions, and (2) are unsuitable for modeling lo…
Evolving Losses for Unsupervised Video Representation Learning
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
We present a new method to learn video representations from large-scale unlabeled video data. Ideally, this representation will be generic and transferable, directly usable for new…
Model-based Behavioral Cloning with Future Image Similarity Learning
Alan Wu, AJ Piergiovanni, Michael S. Ryoo
We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-po…
Evolving Losses for Unlabeled Video Representation Learning
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
We present a new method to learn video representations from unlabeled data. Given large-scale unlabeled video data, the objective is to benefit from such data by learning a generic…