Cross-dimensional Weighting for Aggregated Deep Convolutional Features

Understand

We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs.

• We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps.
• We then propose specific non-parametric schemes for both spatial- and channel-wise weighting that boost the effect of highly active spatial responses and at the same time regulate burstiness effects.
• We experiment on different public datasets for image search and show that our approach outperforms the current state-of-the-art for approaches based on pre-trained networks.