Distillation Improves Visual Place Recognition for Low-Quality Queries
Anbang Yang Yao Wang John-Ross Rizzo Chen Feng
The shift to online computing for real-time visual localization often
requires streaming query images/videos to a server for visual place recognition
(VPR), where fast video transmission may result in reduced resolution or
increased quantization. This compromises the quality of global image
descriptors, leading to decreased VPR performance. To improve the low recall
rate for low-quality query images, we present a simple yet effective method
that uses high-quality queries only during training to distill better feature
representations for deep-learning-based VPR, such as NetVLAD. Specifically, we
use mean squared error (MSE) loss between the global descriptors of queries
with different qualities, and inter-channel correlation knowledge distillation
(ICKD) loss over their corresponding intermediate features. We validate our
approach using the both Pittsburgh 250k dataset and our own indoor dataset with
varying quantization levels. By fine-tuning NetVLAD parameters with our
distillation-augmented losses, we achieve notable VPR recall-rate improvements
over low-quality queries, as demonstrated in our extensive experimental
results. We believe this work not only pushes forward the VPR research but also
provides valuable insights for applications needing dependable place
recognition under resource-limited conditions.