CPT creates cluster-invariant spaces from pre-trained VLM semantics and applies neural collapse losses to boost long-tail performance and unseen-class generalization in prompt tuning.
Capt: Class-aware prompt tuning for federated long-tailed learning with vision- language model
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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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Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models
CPT creates cluster-invariant spaces from pre-trained VLM semantics and applies neural collapse losses to boost long-tail performance and unseen-class generalization in prompt tuning.
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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.