  Retrieval augmented models are becoming increasingly popular for computer
vision tasks after their recent success in NLP problems. The goal is to enhance
the recognition capabilities of the model by retrieving similar examples for
the visual input from an external memory set. In this work, we introduce an
attention-based memory module, which learns the importance of each retrieved
example from the memory. Compared to existing approaches, our method removes
the influence of the irrelevant retrieved examples, and retains those that are
beneficial to the input query. We also thoroughly study various ways of
constructing the memory dataset. Our experiments show the benefit of using a
massive-scale memory dataset of 1B image-text pairs, and demonstrate the
performance of different memory representations. We evaluate our method in
three different classification tasks, namely long-tailed recognition, learning
with noisy labels, and fine-grained classification, and show that it achieves
state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.
