ATTENTION-BASED OBJECT LOCALIZATION USING CONVOLUTIONAL BLOCK ATTENTION MODULE AND FREQUENT ITEMSET MINING

Abstract

Object localization is one of the core tasks in computer vision, as they are applied in many real-world applications such as autonomous vehicles and robotics. It refers to the task of locating an object in an image us ing a bounding box. Most of the existing object localization methods require a huge amount of annotations for training and are highly time consuming. Thus, it is worth developing object localization methods for unlabeled images. However, this is far more challenging than typical co localization or weakly super- vised localization tasks. To tackle this prob lem, a novel attention-based method is proposed that takes advantage of CNN models, attention mechanisms, and data mining. Specifically, the proposed method first converts the feature maps from a new feature map extractor model,VggCBAM, into a set of transactions and then discovers frequent patterns from the transaction database through pattern mining techniques. From the experimental results, it is observed that the fea ture maps extracted contain meaningful activations that increase focus on the object of interest while suppressing background and the discovered patterns typically hold appearance and spatial consistency. Motivated by observation, this method can easily discover and localize possible objects by merging meaningful patterns. This approach does not need any anno tations yet still shows promising localization ability, which provides a new perspective to solve the localization problem.