Batch Transformer

Batch Transformers are a novel approach in deep learning that leverages attention mechanisms across the batch dimension of input data, rather than within individual samples. This allows the model to learn relationships between samples within a mini-batch, improving robustness to data scarcity issues like class imbalance and domain shift. Current research focuses on developing and applying BatchFormer architectures to various visual recognition tasks, including image classification, object detection, and panoptic segmentation, demonstrating consistent performance improvements over traditional methods. The ability to learn from sample relationships offers significant potential for enhancing the generalization and reliability of deep learning models across diverse applications.