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.

研究使用检索增强模型来提高计算机视觉任务的识别能力，引入了一种基于注意力机制的记忆模块，学习从外部记忆集合中检索出的每个例子的重要性，证明了使用 10 亿个图像 - 文本对的大规模记忆数据集的好处，并在 ImageNet-LT，Places-LT 和 Webvision 数据集中取得了最先进的准确性。