Public articles linked to the same research event.
arXiv PromptGate introduces a client-adaptive vision-language gating module for open-set federated active learning (OS-FAL): it learns class-specific context (CSC) prompts on a frozen BiomedCLIP backbone, splitting them into global tokens aggregated via FedAvg and client-local tokens, uses VLM pseudo-labels to filter the unlabeled pool into a high-purity ID candidate pool before querying, and then hands that pool to any downstream active learning strategy; on the FedISIC and FedEMBED federated medical imaging benchmarks, static VLM prompting degrades to roughly 50% ID purity, whereas PromptGate maintains above 95% purity with 98% OOD recall.
PromptGate introduces a client-adaptive vision-language gating module for open-set federated active learning (OS-FAL): it learns class-specific context (CSC) prompts on a frozen BiomedCLIP backbone, splitting them into global tokens aggregated via FedAvg and client-local tokens, uses VLM pseudo-labels to filter the unlabeled pool into a high-purity ID candidate pool before querying, and then hands that pool to any downstream active learning strategy; on the FedISIC and FedEMBED federated medical imaging benchmarks, static VLM prompting degrades to roughly 50% ID purity, whereas PromptGate maintains above 95% purity with 98% OOD recall.
PromptGate introduces a client-adaptive vision-language gating module for open-set federated active learning (OS-FAL): it learns class-specific context (CSC) prompts on a frozen BiomedCLIP backbone, splitting them into global tokens aggregated via FedAvg and client-local tokens, uses VLM pseudo-labels to filter the unlabeled pool into a high-purity ID candidate pool before querying, and then hands that pool to any downstream active learning strategy; on the FedISIC and FedEMBED federated medical imaging benchmarks, static VLM prompting degrades to roughly 50% ID purity, whereas PromptGate maintains above 95% purity with 98% OOD recall.
PromptGate introduces a client-adaptive vision-language gating module for open-set federated active learning (OS-FAL): it learns class-specific context (CSC) prompts on a frozen BiomedCLIP backbone, splitting them into global tokens aggregated via FedAvg and client-local tokens, uses VLM pseudo-labels to filter the unlabeled pool into a high-purity ID candidate pool before querying, and then hands that pool to any downstream active learning strategy; on the FedISIC and FedEMBED federated medical imaging benchmarks, static VLM prompting degrades to roughly 50% ID purity, whereas PromptGate maintains above 95% purity with 98% OOD recall.