BiCo: Bilevel Collaborative Learning for Low-Light Vision

Low-light conditions simultaneously impair visual quality and semantic perception, posing a fundamental challenge to robust vision in real-world environments. Rather than treating enhancement and high-level perception as isolated tasks, we propose a bilevel collaborative framework to explicitly coordinate pixel-level and semantic-level objectives for low-light vision. We first revisit the pixel-semantic gap and establish learnable role-balanced evaluators for the two levels, where pretrained foundation-model priors provide complementary quality and semantic guidance. Building upon these evaluators, we formulate the enhancer as the upper-level variable and the pixel- and semantic-level evaluators as lower-level variables, enabling reciprocal feedback between enhancement and perception through bilevel optimization. We further develop a two-stage learning strategy: cross-task collaboration learns a transferable enhancer through joint pixel-semantic optimization, while task-specific adaptation specializes the semantic network to a target downstream task with the enhancer fixed. The resulting enhancer captures task-agnostic meta-representations that generalize across different semantic tasks, including those unseen during collaborative learning. Extensive experiments on low-light detection and segmentation demonstrate consistent improvements in both visual quality and semantic performance, together with strong cross-task adaptability and generalization. The code will be released upon acceptance.