## 1 Introduction
(2021) demonstrated that combining different architectures in a single model can be effective for weakly supervised video anomaly detection (WS-VAD) [16]. This model extracts features using pre-trained models based on Vision Transformer (ViT), Contrastive Language Image Pretraining (CLIP), and Convolutional Neural Network (CNN). This comprehensive approach of combining different feature extraction methods and attention mechanisms provides excellent performance in video anomaly detection tasks. Thus, the multi-backbone approach allows for improved performance by combining different backbones into a single model using the same training dataset. The multi-backbone approach allows multiple input types to be used in a single model. Different architectures can be trained on different input sources, and the results can be combined to produce more accurate results. [PERSON] et al. (2021) introduced DKDMN, a data- and knowledge-driven deep multi-view network for hyperspectral image classification [17]. DKDMN uses a multi-channel integration classification framework (MBICF) to effectively capture multi-scale spectral and local-global features. By combining raw hyperspectral data with knowledge obtained through a diffusion model-based learning model, DKDMN achieves competitive classification accuracy, outperforming SOTA methods on several hyperspectral image datasets. In addition, recently, several promising approaches have appeared that use tensor-based graph learning models to address certain limitations in existing methods for processing high-dimensional data in various applications [18; 19]. This is a very intriguing approach that could be very promising for improving performance by leveraging high-order relationships among viewpoints. Additionally, each input source can be enhanced using a channel enhancement technique. [PERSON] et al. (2024) demonstrated that this approach can be easily integrated into existing addition operations, continuously improving robustness to color variations. The research proposed channel augmentation for visible-infrared re-identification, where the basic idea is to generate color-irrelevant images by randomly exchanging color channels [20; 21]. For cross-modality metric learning, an enhanced channel-mixed learning strategy was designed to simultaneously handle intra- and cross-modality variations, resulting in stronger discriminability. There are numerous augmentation techniques which are used for data augmentation. The same techniques are available for channel augmentation in multi-backbone models, so it was decided to compare the techniques with the best performance in this paper. [PERSON] et al. (2019) prepared a comparative analysis of the augmentation of different augmentation methods and proposed AutoAugment, a procedure for automatically searching for improved data augmentation policies [22]. They developed a search space where a policy consists of many sub-policies, one of which is randomly selected for each image in each mini-packet. The operations included in the search are ShearX/Y, TranslateX/Y, rotate, autocontrast, invert, equalize, solarize, posterize, contrast, color, brightness, sharpness, cutout, and sample pairing. The results show that the most frequently selected transformations on CIFAR-10 are equalization, autocontrast, color, and brightness. These methods were selected for comparative analysis and simulating low-light conditions in this paper. Recent studies examine how different data formats affect the model's prediction results. Most image formats are designed to display images on displays, but they are not specifically designed for computer vision. In addition, most images are pre-processed by the provider to modify the resulting image. The main goal of processing is to make the image more familiar and pleasing to the human eye. Different cameras have different ISPs and pre-processing configurations, which can affect the quality of predictions when using CV solutions, as pre-processing can change or even discard some useful information for CV. [PERSON] et al.