  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 attention in order to better learn the importance of
features at each convolutional block of the network. A new network component
named peer-attention is introduced, which dynamically learns the attention
weights using another block or input modality. Even without pre-training, our
models outperform the previous work on standard public activity recognition
datasets with continuous videos, establishing new state-of-the-art. We also
confirm that our findings of having neural connections from the object modality
and the use of peer-attention is generally applicable for different existing
architectures, improving their performances. We name our model explicitly as
AssembleNet++. The code will be available at:
https://sites.google.com/corp/view/assemblenet/
