Papers › Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition
Linchao Zhu, Yi Yang
There have been increasing interests in modeling long-tailed data. Unlike artificially collected datasets, long-tailed data are naturally existed in the real-world and thus more realistic. To deal with the class imbalance problem, we introduce an Inflated Episodic Memory (IEM) for long-tailed visual recognition. First, our IEM augments the convolutional neural networks with categorical representative features for rapid learning on tail classes. In traditional few-shot learning, a single prototype is usually leveraged to represent a category. However, long-tailed data has higher intra-class variances. It could be challenging to learn a single prototype for one category. Thus, we introduce IEM to store the most discriminative feature for each category individually. Besides, the memory banks are updated independently, which further decreases the chance of learning skewed classifiers. Second, we introduce a novel region self-attention mechanism for multi-scale spatial feature map encoding. It is beneficial to incorporate more discriminative features to improve generalization on tail classes. We propose to encode local feature maps at multiple scales, and the spatial contextual information should be aggregated at the same time. Equipped with IEM and region self-attention, we achieve state-of-the-art performance on four standard long-tailed image recognition benchmarks. Besides, we validate the effectiveness of IEM on a long-tailed video recognition benchmark, i.e., YouTube-8M.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-tail Learning | ImageNet-LT | IEM | Top-1 Accuracy | 43.2 | #59 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | IEM | Top-1 Accuracy | 39.7 | #18 of 29 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | IEM | Top-1 Accuracy | 70.2 | #32 of 43 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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