Convolutional Neural Networks (CNNs) are one of the most commonly used architectures for image-related deep learning studies. Despite its popularity, CNNs have some intrinsic limitations such as losing some of the spatial information and not being robust to affine transformations due to pooling operations. On the other hand, Capsule Networks are composed of groups of neurons, and with the help of its novel routing algorithms, they have the capability for learning high dimensional pose configuration of the objects as well. In this study, we investigate the performance of brand-new Capsule Networks using dynamic routing algorithm on the clothing classification task. To achieve this, we propose 4-layer stacked-convolutional Capsule Network architecture (FashionCapsNet), and train this model on DeepFashion dataset that contains 290k clothing images over 46 different categories. Thereafter, we compare the category classification results of our proposed design and the other state-of-the-art CNN-based methods trained on DeepFashion dataset. As a result of the experimental study, FashionCapsNet achieves 83.81% top-3 accuracy, and 89.83% top-5 accuracy on the clothing classification. Based upon these figures, FashionCapsNet clearly outperforms the earlier methods that neglect pose configuration, and has comparable performance to the baseline study that utilizes an additional landmark information to recover pose configuration. Finally, in the future, proposed FashionCapsNet may inherit extra performance boost on the clothing classification due to advances in the relatively new Capsule Network research.

White balance (WB) correction is critical for accurate color reproduction in digital images, especially under complex, multi-illuminant lighting conditions. Traditional methods, such as the Gray-World assumption, rely on global statistics and struggle in real-world, non-uniform lighting scenarios. Modern deep learning approaches, including convolutional and attention-based architectures, have significantly advanced WB correction but often fail to explicitly account for higher-order feature distribution statistics, which may limit their robustness in challenging environments. This study introduces a novel framework that leverages Exact Feature Distribution Matching (EFDM) as a loss objective to align feature distributions across multiple moments, including mean, variance, skewness, and kurtosis. By modeling lighting as a style factor, the method explicitly addresses distributional shifts caused by complex illumination, offering a robust solution for WB correction. The framework integrates EFDM with a Vision Transformer architecture, enabling precise handling of global and local lighting variations. Extensive experiments on the large-scale multi-illuminant (LSMI) dataset demonstrate the superiority of the proposed approach over state-of-the-art methods and commonly used loss functions when applied to the same architecture. Qualitative and quantitative evaluations highlight its effectiveness in achieving perceptually accurate WB correction, particularly in multi-illuminant environments. By bridging statistical modeling with modern deep learning, this work establishes the critical role of feature distribution alignment in advancing WB correction and sets a new benchmark for robustness and generalization in complex lighting scenarios.