# Learning to Segment Blob-like Objects by Image-Level Counting
## 2 State-of-the-Art Research  
An early work dealing with the possibility of training object localization indirectly with a classification task found that the locations of objects can be approximated in a weakly supervised manner [11]. As a drawback, they noticed a poor determination of the extent of the localized objects.  
More recent work has presented a network architecture and loss functions for weakly supervised training through image-level classifications [4]. The architecture relies on the pre-trained convolutional layers of a VGG16 [12] network as the encoder part, which is kept frozen and, therefore, relies on the knowledge that was acquired in previous strong supervision training. Although the approach yields good results in segmenting common photos with known object classes, the transfer of the technique is limited to domains where a strong basis in the form of pre-trained networks as encoders is available. In contrast, we targeted an approach that does not rely on this prerequisite. Since the general functionality could be shown for a classification task, we present and evaluated a training method for image-level counting as an annotation for cases with potentially high numbers of objects, and we, then, applied it to different types of network architectures.  
A similar field of application is in crowd counting, in which the number of people present in images showing crowds is to be determined. Due to a large number of possible use cases, this topic has attracted great attention in recent years [2; 3; 13]. Most of the crowd-counting approaches, however, rely not only on the number of objects in an image directly, but also require segmentation or key points--often in the form of a density map or dot map--which encode the centers of the objects [3]. Training purely by counts of objects is also found among crowd-counting approaches, but is usually used for training a counter directly and not for learning to segment. In addition, the segmentations from the intermediate activation maps tend to lack sharp object borders.  
Hybrid approaches use multiple levels of annotations in parallel, thus rely on a localized annotation. Some recent models employ vision transformer networks [14], but most approaches are based on convolutional neural networks (CNNs) since transformer-based solutions often need significantly more computation steps and memory while improving accuracies non-substantially [3]. Prominent examples of these categories are the CSRNet [15] (CNN-based) and TransCrowd [16] (transformer-based) architectures.  
Solutions for image segmentation with small objects often employ a UNet [17] or architectures based on it [18]. Examples of successors are UNet++ [19], which establishes a stronger connection between the single convolutional steps than in the original structure, and TransUNet [16], which uses the encoding capabilities of transformers [20] for the encoder part of the network.  
For the goal of obtaining a segmentation from training a counter, class activation mapping (CAM) [5] has to be mentioned as a set of methods that can contribute. They are used in the context of explainable AI [21] by examining which parts of the input have led to the final result. In this way, a classification decision is visualized that would otherwise be presented to a user without any explanation. An early and prominent method from this category of techniques is Grad-CAM, which visualizes class activation maps using gradient information [22]. Extensions and modifications of this method emerged that were able to improve the visualizations for specific use cases. Examples of that are Grad-CAM++ [23], Full-Grad [24], HiRes-CAM [25], and Layer-CAM [26]. Methods that were inspired by Grad-CAM, but that work without access to the gradients are, for example, Eigen-CAM [27], Ablation-CAM [28], and Score-CAM [29].