OmniGCD trains a Transformer once on synthetic data to enable zero-shot generalized category discovery across 16 datasets in four modalities without any dataset-specific fine-tuning.
Kingma and Jimmy Ba
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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FedHarmony harmonizes heterogeneous label correlations in federated multi-label learning via consensus correlations as global teachers and quality-weighted aggregation, with an accelerated optimizer that converges faster while improving accuracy over prior methods.
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OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism
OmniGCD trains a Transformer once on synthetic data to enable zero-shot generalized category discovery across 16 datasets in four modalities without any dataset-specific fine-tuning.
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FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
FedHarmony harmonizes heterogeneous label correlations in federated multi-label learning via consensus correlations as global teachers and quality-weighted aggregation, with an accelerated optimizer that converges faster while improving accuracy over prior methods.