# Weakly Supervised Learning for Object Localization Based on an Attention Mechanism
###### Abstract  
Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large size of the dataset make this a laborious task. Hence, it is of paramount importance that the expensive work associated with the tasks involving strong labeling, such as bounding box annotation, is reduced. In this paper, we propose a method to perform object localization tasks without bounding box annotation in the training process by means of employing a two-path activation-map-based classifier framework. In particular, we develop an activation-map-based framework to judicially control the attention map in the perception branch by adding a two-feature extractor so that better attention weights can be distributed to induce improved performance. The experimental results indicate that our method surpasses the performance of the existing deep learning models based on weakly supervised object localization. The experimental results  
Weakly Supervised Segmentation (WSS) [5; 6] performs segmentation by receiving a bounding box or a scribble annotation as a label without pixel level annotation. In WSL, 3D object reconstruction performs reconstruction task [7] using only the pixel or bounding-box level instead of voxel or mesh level annotation. Weakly Supervised Object Localization (WSOL) aims to localize an object as well as classify it without expensive labelling. That is, WSOL identifies the locations of objects by training the model without the need for bounding box annotations of the locations of these objects in an image. When training a deep learning model without bounding box annotations, localizing the object becomes difficult using only the classifiers of Convolutional Neural Networks (CNNs) because the feature extractor of a CNN learns only the features useful for classifying objects. That is, the classifier learns only the characteristic part of the input images and falls into sub-optimal localization. Therefore, for example, when a deep learning model identifies a bird's position, it localizes only the peak or face, which are important features of a bird. An example of this is shown in Figure 1.  
In order to solve this problem, a previous paper [8] proposed an approach involving the application of the regional dropout method to the input data, and several papers [9; 10; 11] have proposed various methods to solve the sub-optimal localization problem. However, in WSOL, this problem is still a difficult issue to solve.  
In this paper, we propose a method of adjusting the classifier weights to solve the sub-optimal problem with greater efficiency than the existing method. Our proposed method is composed of two classifiers, and the features reduced from those that are intensively used by the first classifier are given as inputs to the second classifier so that the wider area of an object can be learned.  
Our contributions are as follows:  
* There is no pre-processing task, such as hiding part of an image, required before training the model. Therefore, it does not take extra time to train the model.  
Figure 1: Sub-optimal localization problems: using the CAM [1] method alone, birds’ tails, wings, and legs are not localized.  
* The number of learning parameters is minimized without an additional layer to solve the sub-optimal problem of WSOL, but the proposed method shows better performance than the existing methods.
* Some of the existing studies had to inject the same input several times to obtain the attention result for one image at the inference stage, but our method can obtain the attention with only one input.  
The rest of the paper is composed as follows. Section 2 describes the related research work, while Section 3 describes the proposed method. Section 4 presents and discusses the experimental test results and Section 5 provides the concluding remarks.