## 2 Related Works  
The challenge inherent in any image identification system is attempting for 100% efficiency, a goal often elusive due to intrinsic complexities. This is particularly true in endeavors such as identifying physical violence, where false negatives are regrettably common. To address this challenge, our research adopts an active learning approach, mixing the collaborative efforts of human experts and machine learning algorithms. Active learning serves as a strategic framework aimed at refining classifiers to generalize well on instances not encountered within the problem domain [33]. Active learning finds its application in various domains, including image classification and object detection. [PERSON] and [PERSON] [34] propose a method that employs a measure of uncertainty, coupled with information density and an adaptive information framework, to select instances from unlabeled sets based on their informativeness. By combining measures of uncertainty with adaptive information density, this approach effectively guides the selection of instances for labeling, thereby enriching the labeled dataset. [PERSON] et al. [35] investigate and compare different active learning methods in the context of image classification using high-dimensional data and CNNs. The paper evaluates ensemble-based methods against Monte Carlo dropout and geometric approaches, finding that ensembles perform better and provide more calibrated predictive uncertainties, which are crucial for active learning algorithms. The study includes experiments on various datasets, including MNIST, CIFAR-10, and ImageNet, as well as a large, class-imbalanced diabetic retinopathy dataset. The results demonstrate that ensemble-based active learning is particularly effective in managing class imbalance during the acquisition phase. In [36], [PERSON] and [PERSON] introduce a core-set selection approach for active learning in CNNs, providing a theoretical framework to evaluate subset performance and demonstrating significant improvements in image classification tasks over existing methods. This method involves choosing a subset of data points such that a model trained on this subset performs competitively on the remaining data. [PERSON] et al. [37] produce significantly different results for a given sample based on the principle that if two deep neural networks (DNNs) of the same structure are trained on the same dataset, that sample should be selected for additional training. The dual active sampling on batch-incremental active learning approach has proven useful. It simplifies implementation and reduces computational requirements compared to other state-of-the-art methods, leading to better results on the CIFAR-10 dataset with reduced computational time compared to the core-set method. [PERSON] et al. [38] introduce new methods for bag-level aggregation in multiple instances of active learning (MIAL), a technique used in instance classification problems. The study focuses on reducing labeling costs by identifying the most informative instances and querying the expert for their labels. The proposed method outperforms existing methods in various application domains, including aggregated informativeness and cluster-based aggregative sampling. This research is significant as it addresses the limitations of single-instance active learning methods in multiple-instance learning problems. Recent efforts, exemplified by [PERSON] et al. [30], emphasize using large datasets without full labeling. [PERSON] et al. propose a method for object detection that amalgamates active and supervised learning techniques. Initially, labeled data are utilized to train a detection model in a semi-supervised manner. Subsequently, the stability of the unlabeled data is assessed, with low-stability instances manually labeled, while those with high uncertainty are pseudo-labeled using predictions from the detector. Integrating manual and pseudo-labeling contributes to refining the detection model, yielding promising results with a reported mean average precision (mAP) of 79.2%.