Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions
share
Pre-training fine-tuning is a prevalent paradigm in computer vision (CV). Recently, parameter-efficient transfer learning (PETL) methods have shown promising performance in transferring knowledge from pre-trained models with only a few trainable parameters. Despite their success, the existing PETL methods in CV can be computationally expensive and require large amounts of memory and time cost during training, which limits low-resource users from conducting research and applications on large models. In this work, we propose Parameter, Memory, and Time Efficient Visual Adapter (E^3VA) tuning to address this issue. We provide a gradient backpropagation highway for low-rank adapters which removes large gradient computations for the frozen pre-trained parameters, resulting in substantial savings of training memory and training time. Furthermore, we optimise the E^3VA structure for dense predictions tasks to promote model performance. Extensive experiments on COCO, ADE20K, and Pascal VOC benchmarks show that E^3VA can save up to 62.2 comparable performance to full fine-tuning and better performance than most PETL methods. Note that we can even train the Swin-Large-based Cascade Mask RCNN on GTX 1080Ti GPUs with less than 1.5
READ FULL TEXT