  Meta-learning is widely used in few-shot classification and function
regression due to its ability to quickly adapt to unseen tasks. However, it has
not yet been well explored on regression tasks with high dimensional inputs
such as images. This paper makes two main contributions that help understand
this barely explored area. \emph{First}, we design two new types of
cross-category level vision regression tasks, namely object discovery and pose
estimation of unprecedented complexity in the meta-learning domain for computer
vision. To this end, we (i) exhaustively evaluate common meta-learning
techniques on these tasks, and (ii) quantitatively analyze the effect of
various deep learning techniques commonly used in recent meta-learning
algorithms in order to strengthen the generalization capability: data
augmentation, domain randomization, task augmentation and meta-regularization.
Finally, we (iii) provide some insights and practical recommendations for
training meta-learning algorithms on vision regression tasks. \emph{Second}, we
propose the addition of functional contrastive learning (FCL) over the task
representations in Conditional Neural Processes (CNPs) and train in an
end-to-end fashion. The experimental results show that the results of prior
work are misleading as a consequence of a poor choice of the loss function as
well as too small meta-training sets. Specifically, we find that CNPs
outperform MAML on most tasks without fine-tuning. Furthermore, we observe that
naive task augmentation without a tailored design results in underfitting.
