The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.

Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters.

Deep learning models have been widely used to assist doctors with clinical decision-making.

Notwithstanding astonishing advances in computer vision technologies, detecting ships and floating matters in these images is challenging due to factors such as object distance.