CUE mitigates concept confusion in long-tailed visual recognition by expanding supervision with multi-label concept sets from zero-shot CLIP and LLMs, using auxiliary Binary Logit-Adjustment losses to achieve stronger balanced performance than prior methods.
Improving diffusion models for class-imbalanced training data via capacity manipulation
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Fine-tuning impairs the class balance of foundation models in long-tailed personalized federated learning, which FedPuReL addresses through gradient purification using zero-shot predictions and residual-based personalization to achieve better global and local performance.
citing papers explorer
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CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning
CUE mitigates concept confusion in long-tailed visual recognition by expanding supervision with multi-label concept sets from zero-shot CLIP and LLMs, using auxiliary Binary Logit-Adjustment losses to achieve stronger balanced performance than prior methods.
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Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Fine-tuning impairs the class balance of foundation models in long-tailed personalized federated learning, which FedPuReL addresses through gradient purification using zero-shot predictions and residual-based personalization to achieve better global and local performance.