Papers › Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
Kaihua Tang, Jianqiang Huang, Hanwang Zhang
As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image. Therefore, long-tailed classification is the key to deep learning at scale. However, existing methods are mainly based on re-weighting/re-sampling heuristics that lack a fundamental theory. In this paper, we establish a causal inference framework, which not only unravels the whys of previous methods, but also derives a new principled solution. Specifically, our theory shows that the SGD momentum is essentially a confounder in long-tailed classification. On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head. On the other hand, its induced mediation also benefits the representation learning and head prediction. Our framework elegantly disentangles the paradoxical effects of the momentum, by pursuing the direct causal effect caused by an input sample. In particular, we use causal intervention in training, and counterfactual reasoning in inference, to remove the "bad" while keep the "good". We achieve new state-of-the-arts on three long-tailed visual recognition benchmarks: Long-tailed CIFAR-10/-100, ImageNet-LT for image classification and LVIS for instance segmentation.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-tail Learning | CIFAR-10-LT (ρ=10) | Causal Norm | Error Rate | 11.5 | #36 of 50 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-10-LT (ρ=10) | DecTDE | Error Rate | 12.63 | #44 of 50 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | De-confound-TDE | Top-1 Accuracy | 51.8 | #51 of 69 | 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.
Methods
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