Contrastive Forward-Forward: A Training Algorithm of Vision Transformer

A new approach called Multi-Scale Visual Prompting (MSVP) has been introduced to enhance small-image classification tasks, utilizing lightweight, learnable parameters integrated into the input space. This method significantly improves performance across various convolutional neural networks (CNN) and Vision Transformer architectures while maintaining a minimal increase in parameters.

A new technique called ViT-SR has been introduced for Single Image Super-Resolution (SISR), utilizing a two-stage training strategy that incorporates self-supervised pretraining on a colorization task followed by adjustment for 4x super-resolution. This method simplifies residual learning by predicting high-frequency residual images added to initial bicubic interpolations, achieving notable performance metrics on the DIV2K benchmark dataset.

The ALDI++ framework has been adapted for security X-ray object detection, addressing the challenges posed by domain adaptation in real-world applications. This adaptation is crucial due to the significant variations in scanning devices and environmental conditions that can degrade model performance, as demonstrated through extensive experiments on the EDS dataset.