## 1 Introduction  
Multi-label image classification is a task in which multiple classes are identified from a given image. For instance, when presented with an image that contains both a cat and a dog, the task is to predict the presence of both these classes (cat and dog) in the image. This task has applications in various domains, including autonomous vehicles [1], fashion [2], medical imaging [3], visual content tagging [4], and education [5]. Many different approaches have been proposed for this task. Recent methods, in particular, emphasize enhancing multi-label classification accuracy, especially by leveraging semantic label relationships or consistencies [6; 7; 8; 9]. Clearly, these strategies have significantly improved multi-label image classification accuracy, especially on well-known datasets, such as MS-COCO [10], Pascal VOC [11], and Fashion550K [2]. In specific application domains, one-shot learning [12] and class-incremental learning [13] are essential for multi-label image classification. In this context, \"one-shot\" means that only a single sample is used to train each class, while \"class-incremental\" indicates that new classes are added to the model over time. A common example is in smartphone gallery applications, where users annotate images of new acquaintances. In this situation, it is natural for users to annotate just one image for each new acquaintance, illustrating the \"one-shot\" approach. Additionally, as new acquaintances are added as new classes, \"class-incremental\" learning becomes necessary. Despite the limited annotated data available for these emerging classes, such as new acquaintances, the gallery application must still effectively perform multi-label image classification. Another example can be found in primary school computer science and machine learning education. Here, young students update their machine learning models by adding or removing classes based on just one or a few samples [5; 14]. Unfortunately, most of the existing approaches do not focus on one-shot learning or class-incremental learning for multi-label image classification. Instead, many existing methods concentrate on classifying widely recognized datasets, such as MS-COCO. Although there has been a recent paper introducing the term \"few-shot class-incremental learning [15]\", it does not consider situations where the total number of data is very limited or when entirely new types of datasets are used. As a result, there is a need for a novel approach. We delineate our unique requirements for one-shot class-incremental multi-label image classification as follows:  
* Requirement 1: Our model must be capable of rapid adaptation when new classes of images are added, a process we refer to as \"class-incremental learning\". Conversely, it should also be equipped with the ability to forget specific classes, a concept opposite to class-incremental learning, which we denote in this paper as \"class-decremental learning\". * Requirement 2: Our model must perform multi-label classification effectively, even when presented with a limited number of data. Furthermore, it should allow for easy insertion or deletion of a small number of training samples. * Requirement 3: Our model should be able to manage unpopular or unexpected image combinations that do not reflect the real world. For instance, even when our input images contain elements like dolphins and grass together, which is an unnatural and unexpected combination, our model should still perform well on these unconventional datasets. * Requirement 4: Our model must be lightweight to ensure that it runs efficiently on low-performance computers, including laptops with basic GPUs. This is a critical requirement for certain application domains where only low-end hardware is available. For instance, in K-12 education settings, low-performance computers are commonly used. Despite these constraints, our model should still deliver fast performance. In this paper, we present a straightforward yet effective approach to satisfying the aforementioned requirements.