## 3 Dataset Construction  
In this section, we build a dataset for one-shot multi-label image classification. To meet the requirements introduced earlier, our goal is to construct a dataset comprising many classes, with a very limited number of samples within each class.  
Recall that one of the application domains for this task is computer science education for children. Therefore, we decided to collect data from Scratch [22; 23], one of the world's most popular children's programming environments. Within Scratch, there are two main types of images: backdrop images and character images. The characters are referred to as \"sprites\" in Scratch terminology.  
Our idea for constructing the dataset for this task involves randomly placing multiple sprites on random backdrops. These arbitrary placements result in very noisy merged images, making the task challenging for multi-label image classification algorithms. However, such noisy images are commonly found in children's Scratch programs.  
We collected all of the backdrop images from Scratch, a total of 85 images. These collected backdrop images are represented in Figure 1.  
In Scratch, there are hundreds of sprite images, but some images are nearly identical, as one sprite can have multiple images with slight modifications to represent character movements in animations. To clarify the problem, we randomly selected 100 sprite images and left only one image for each sprite. We also deleted sprite images that had copyright issues when distributed. As a result, we obtained 69 sprite images, as shown in Figure 2.  
The next step was to place the sprites randomly on random backdrops. However, the sizes of the sprite images vary widely, and they may overlap, causing one image to be hidden in the merged image. To prevent this, we imposed a restriction that the width and  
Figure 1: A total of 85 backdrop images collected from the Scratch programming environment.  
Figure 2: A total of 69 sprite images selected from the Scratch programming environment.  
height of any sprite should not exceed 200 pixels. Additionally, we ensured that the x or y coordinates of any two sprite images were not less than 200 pixels apart. Within these constraints, we randomly varied the size of each sprite image from 100% to 200%.  
The constructed dataset includes three types of merged images, with three, six, or nine sprite images placed on each backdrop, respectively. For example, the first column of subfigures in Figure 3 represents the randomly selected backdrop images, and the second column of subfigures in Figure 3 represents the nine sprite images placed on each backdrop. In this figure, our task is to find only the nine sprites we randomly placed. Obviously, this is challenging because there are other objects that the backdrop already has. For example, in the second row of subfigures, what we want to do is not find objects like a bed, a pillow, a mirror, or a monitor but find items such as a drum, a hat, a skeleton, etc. The constructed dataset can be downloaded at [[https://github.com/tooeeworld/mlic_dataset](https://github.com/tooeeworld/mlic_dataset)]([https://github.com/tooeeworld/mlic_dataset](https://github.com/tooeeworld/mlic_dataset)) (accessed on 19 September 2023.).  
In summary, we constructed a new dataset that is fundamentally different from any existing datasets. As a result, this new dataset can be considered \"unseen\" or \"novel\", making it ideal for evaluating one-shot class-incremental approaches without overlapping with the data used to train existing neural network models.