Adversarial Complementary Learning for Weakly Supervised Object Localization
This addresses the problem of localizing objects with weak supervision for computer vision applications, representing a strong specific gain rather than a foundational advancement.
The paper tackles weakly supervised object localization by proposing Adversarial Complementary Learning (ACoL), which uses two parallel classifiers to dynamically erase and discover complementary object regions, achieving a Top-1 localization error rate of 45.14% on the ILSVRC dataset, setting a new state-of-the-art.
In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objects of semantic interest with weak supervision. We first mathematically prove that class localization maps can be obtained by directly selecting the class-specific feature maps of the last convolutional layer, which paves a simple way to identify object regions. We then present a simple network architecture including two parallel-classifiers for object localization. Specifically, we leverage one classification branch to dynamically localize some discriminative object regions during the forward pass. Although it is usually responsive to sparse parts of the target objects, this classifier can drive the counterpart classifier to discover new and complementary object regions by erasing its discovered regions from the feature maps. With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together. The merits of ACoL are mainly two-fold: 1) it can be trained in an end-to-end manner; 2) dynamically erasing enables the counterpart classifier to discover complementary object regions more effectively. We demonstrate the superiority of our ACoL approach in a variety of experiments. In particular, the Top-1 localization error rate on the ILSVRC dataset is 45.14%, which is the new state-of-the-art.