Bottleneck Transformer

Bottleneck Transformers are a class of neural network architectures designed to efficiently process and fuse information from multiple modalities, such as audio and video, or different representations of the same data (e.g., images and voxels). Current research focuses on applying these architectures to diverse tasks, including multimodal classification, human pose estimation, and medical image analysis, often incorporating techniques like self-supervised pre-training and contrastive learning to improve performance and reduce computational demands. The resulting models demonstrate improved accuracy and efficiency compared to traditional approaches in various applications, highlighting the potential of Bottleneck Transformers for advancing fields ranging from computer vision and audio processing to healthcare diagnostics.