  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 descriptor. Importantly, the learnable
parameters of our activation blocks are explicitly trained, together with the
CNN parameters, in an end-to-end manner minimising triplet loss. This means
that our network jointly learns the CNN filters and their optimal activation
and aggregation for retrieval tasks. To our knowledge, this is the first time
parametric functions have been used to control and learn optimal aggregation.
We conduct an in-depth experimental study on three non-linear activation
functions: Sine-Hyperbolic, Exponential and modified Weibull, showing that
while all bring significant gains the Weibull function performs best thanks to
its ability to equalise strong activations. The results clearly demonstrate
that our ACTNET architecture significantly enhances the discriminative power of
deep features, improving significantly over the state-of-the-art retrieval
results on all datasets.
