BRAIN uses bias-mitigation continual learning with a new de-bias contrastive loss and angular forgetting mitigation to achieve SOTA performance on vision-brain understanding benchmarks despite brain signal inconsistencies across sessions.
Language guided concept bottleneck models for interpretable continual learning
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A taxonomy survey of continual learning for vision-language models, grouping methods into multi-modal replay, cross-modal regularization, and parameter-efficient adaptation, with a review of benchmarks and metrics.
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BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding
BRAIN uses bias-mitigation continual learning with a new de-bias contrastive loss and angular forgetting mitigation to achieve SOTA performance on vision-brain understanding benchmarks despite brain signal inconsistencies across sessions.
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Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
A taxonomy survey of continual learning for vision-language models, grouping methods into multi-modal replay, cross-modal regularization, and parameter-efficient adaptation, with a review of benchmarks and metrics.