Erasing Integrated Learning : A Simple yet Effective Approach for Weakly Supervised Object Localization

Meng Yang

Abstract:

Weakly supervised object localization (WSOL) aims to
localize object with only weak supervision like image-level
labels. However, a long-standing problem for available
techniques based on the classification network is that they
often result in highlighting the most discriminative parts
rather than the entire extent of object. Nevertheless, trying
to explore the integral extent of the object could degrade
the performance of image classification on the contrary. To
remedy this, we propose a simple yet powerful approach
by introducing a novel adversarial erasing technique, erasing integrated learning (EIL). By integrating discriminative
region mining and adversarial erasing in a single forwardbackward propagation in a vanilla CNN, the proposed EIL
explores the high response class-specific area and the less
discriminative region simultaneously, thus could maintain
high performance in classification and jointly discover the
full extent of the object. Furthermore, we apply multiple
EIL (MEIL) modules at different levels of the network in a
sequential manner, which for the first time integrates semantic features of multiple levels and multiple scales through
adversarial erasing learning. In particular, the proposed
EIL and advanced MEIL both achieve a new state-of-the-art
performance in CUB-200-2011 and ILSVRC 2016 benchmark, making significant improvement in localization while
advancing high performance in image classification