Abstract
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.
Keywords
- Computer Vision: Representation Learning
- Computer Vision: Applications
- Computer Vision: Machine Learning for Vision
- Computer Vision: Motion and Tracking
