AFI-GAN: Improving feature interpolation of feature pyramid networks via adversarial training for object detection

Recent convolutional detectors learn strong semantic features by generating and combining multi-scale features via feature interpolation. However, simple interpolation incurs often noisy and blurred features. To resolve this, we propose a novel adversarially-trained interpolator which can substitute for the traditional interpolation effortlessly. In specific, we design AFI-GAN consisting of an AF interpolator and a feature patch discriminator. In addition, we present a progressive adversarial learning and AFI-GAN losses to generate multi-scale features for downstream detection tasks. However, we can also finetune the proposed AFI-GAN with the recent multi-scale detectors without the adversarial learning once a pre-trained AF interpolator is provided. We prove the effectiveness and flexibility of our AF interpolator, and achieve the better box and mask APs by 2.2% and 1.6% on average compared to using other interpolation. Moreover, we achieve an impressive detection score of 57.3% mAP on the MSCOCO dataset. Code is available at https://github.com/inhavl-shlee/AFI-GAN.