  In this paper, we revive the use of old-fashioned handcrafted video
representations for action recognition and put new life into these techniques
via a CNN-based hallucination step. Despite of the use of RGB and optical flow
frames, the I3D model (amongst others) thrives on combining its output with the
Improved Dense Trajectory (IDT) and extracted with its low-level video
descriptors encoded via Bag-of-Words (BoW) and Fisher Vectors (FV). Such a
fusion of CNNs and handcrafted representations is time-consuming due to
pre-processing, descriptor extraction, encoding and tuning parameters. Thus, we
propose an end-to-end trainable network with streams which learn the IDT-based
BoW/FV representations at the training stage and are simple to integrate with
the I3D model. Specifically, each stream takes I3D feature maps ahead of the
last 1D conv. layer and learns to `translate' these maps to BoW/FV
representations. Thus, our model can hallucinate and use such synthesized
BoW/FV representations at the testing stage. We show that even features of the
entire I3D optical flow stream can be hallucinated thus simplifying the
pipeline. Our model saves 20-55h of computations and yields state-of-the-art
results on four publicly available datasets.
