A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval
[article]
2020
arXiv
pre-print
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio (SNR) of deep convolutional feature maps. Further, we introduce a controlled multi-stream aggregation, where complementary deep features from different convolutional layers are optimally transformed and balanced using our novel activation layers, before aggregation into a global
arXiv:1907.05794v3
fatcat:ukmpjk53wvgq3brok354qqwmim