  This paper presents a novel hybrid representation learning framework for
streaming data, where an image frame in a video is modeled by an ensemble of
two distinct deep neural networks; one is a low-bit quantized network and the
other is a lightweight full-precision network. The former learns coarse primary
information with low cost while the latter conveys residual information for
high fidelity to original representations. The proposed parallel architecture
is effective to maintain complementary information since fixed-point arithmetic
can be utilized in the quantized network and the lightweight model provides
precise representations given by a compact channel-pruned network. We
incorporate the hybrid representation technique into an online visual tracking
task, where deep neural networks need to handle temporal variations of target
appearances in real-time. Compared to the state-of-the-art real-time trackers
based on conventional deep neural networks, our tracking algorithm demonstrates
competitive accuracy on the standard benchmarks with a small fraction of
computational cost and memory footprint.
