A two-stage causal intervention method (TSCNet) improves tail-class accuracy in Vision Transformer long-tailed classification by combining patch and feature-level backdoor adjustment with adaptive counterfactual augmentation.
Global and local mix- ture consistency cumulative learning for long-tailed visual recognitions
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Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification
A two-stage causal intervention method (TSCNet) improves tail-class accuracy in Vision Transformer long-tailed classification by combining patch and feature-level backdoor adjustment with adaptive counterfactual augmentation.