Abstract
Identifying small size images or small objects is a notoriously challenging problem, as discriminative representations are difficult to learn from the limited information contained in them with poor-quality appearance and unclear
object structure. Existing research works usually increase
the resolution of low-resolution image in the pixel space in
order to provide better visual quality for human viewing.
However, the improved performance of such methods is usually limited or even trivial in the case of very small image
size (we will show it in this paper explicitly).
In this paper, different from image super-resolution (ISR), we propose a novel super-resolution technique called
feature super-resolution (FSR), which aims at enhancing
the discriminatory power of small size image in order to
provide high recognition precision for machine. To achieve
this goal, we propose a new Feature Super-Resolution Generative Adversarial Network (FSR-GAN) model that transforms the raw poor features of small size images to highly
discriminative ones by performing super-resolution in the
feature space. Our FSR-GAN consists of two subnetworks: a feature generator network G and a feature discriminator network D. By training the G and the D networks
in an alternative manner, we encourage the G network to
discover the latent distribution correlations between small
size and large size images and then use G to improve the
representations of small images. Extensive experiment results on Oxford5K, Paris, Holidays, and Flick100k datasets
demonstrate that the proposed FSR approach can effectively enhance the discriminatory ability of features. Even when
the resolution of query images is reduced greatly, e.g., 1/64
original size, the query feature enhanced by our FSR approach achieves surprisingly high retrieval performance at
different image resolutions and increases the retrieval precision by 25% compared to the raw query feature.