Evidence-Driven Progressive Knowledge Formation and Selective Adaptation: A Neuroscience-Inspired Continual Learning Framework

Current object detectors suffer from catastrophic forgetting in open-world continual learning, where novel objects emerge progressively from incomplete, noisy, or ambiguous observations. Existing methods typically adapt representations directly to incoming observations, causing immature novel concepts to be prematurely assimilated into established knowledge and destabilizing previously learned representations. Neuroscience suggests that lifelong learning relies on complementary memory systems, where specific experiences are progressively consolidated into generalized knowledge while established representations remain relatively stable. Inspired by this principle, we propose Evidence-driven Progressive Knowledge formation and selective Adaptation (EPKA), a new continual learning framework that transforms uncertain observations into stable knowledge before allowing them to reshape existing representations. EPKA comprises two coupled mechanisms. Progressive Structural Evidence Evolution (PSEE) progressively accumulates and consolidates compatible observations into persistent, structurally coherent novel representations. Adaptive Representation Competition Regulation (ARCR) selectively adapts representations based on their stability, while protecting established knowledge from unnecessary changes. Together, they establish an evidence-knowledge-adaptation process that balances plasticity for emerging concepts with stability of established knowledge. Extensive experiments across three open-world continual learning scenarios and five benchmarks demonstrate that EPKA consistently improves novel knowledge acquisition while mitigating catastrophic forgetting, highlighting its adaptability and plasticity in continuously evolving visual environments.