Papers › Retrieval Augmented Classification for Long-Tail Visual Recognition

Retrieval Augmented Classification for Long-Tail Visual Recognition

22 Feb 2022CVPR 2022 1arXiv:2202.11233archive 2025-07-28

Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton Van Den Hengel

We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. RAC consists of a standard base image encoder fused with a parallel retrieval branch that queries a non-parametric external memory of pre-encoded images and associated text snippets. We apply RAC to the problem of long-tail classification and demonstrate a significant improvement over previous state-of-the-art on Places365-LT and iNaturalist-2018 (14.5% and 6.7% respectively), despite using only the training datasets themselves as the external information source. We demonstrate that RAC's retrieval module, without prompting, learns a high level of accuracy on tail classes. This, in turn, frees the base encoder to focus on common classes, and improve its performance thereon. RAC represents an alternative approach to utilizing large, pretrained models without requiring fine-tuning, as well as a first step towards more effectively making use of external memory within common computer vision architectures.

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Tasks

ClassificationImage ClassificationLong-tail LearningRetrievalimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Long-tail Learning Places-LT RAC (ViT-B-16) Top-1 Accuracy 47.17 #9 of 29 Archive leaderboard report
Long-tail Learning iNaturalist 2018 RAC (ViT-B-16) Top-1 Accuracy 80.24% #6 of 43 Archive leaderboard report

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Methods

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