Cross-dimensional Weighting for Aggregated Deep Convolutional Features
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. We also provide an easy-to-use, open source implementation that reproduces our results.
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Tasks
Datasets
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Image Retrieval | ROxford (Hard) | R – [O] –CroW | mAP | 13.3 | # 20 | |
| Image Retrieval | ROxford (Medium) | R – [O] –CroW | mAP | 42.4 | # 19 | |
| Image Retrieval | RParis (Hard) | R – [O] –CroW | mAP | 47.2 | # 13 | |
| Image Retrieval | RParis (Medium) | R – [O] –CroW | mAP | 70.4 | # 13 |
