Symbiotic Evolutionary Learning for Task-Adaptive Infrared and Visible Image Fusion

Infrared and visible image fusion (IVIF) targets to integrate thermal saliency and rich textures into a single image that is not only visually appealing but also beneficial to downstream vision tasks. However, conventional methods relying on heuristic visual criteria struggle to guarantee task utility. Conversely, task-driven fusion paradigms typically employ fixed-weight scalarization, which suffers from potentially conflicting objectives among heterogeneous tasks, leading to rigid compromises and sub-optimal generalization in multi-task scenarios. To overcome these bottlenecks, this paper proposes a symbiotic evolutionary learning framework for task-adaptive IVIF, termed EvoFuse. Rather than relying on static loss weights, we formulate the fusion-perception correlation from a multi-objective perspective. To structurally instantiate this formulation, we first develop a re-parameterizable fusion architecture that accommodates multi-branch representational capacity during training, yet analytically folds into an ultra-compact single-branch model for efficient inference. To navigate the conflicting multi-task objectives, we introduce an evolutionary search mechanism that dynamically evolves task-aware loss-weight configurations. This enables a mutual adaptation process where the fusion network and task models are jointly updated under a Pareto-inspired non-degradation criterion. Furthermore, a novel saliency discriminative loss is designed to explicitly emphasize semantically crucial regions. Extensive experiments across eleven datasets covering fusion and downstream perception tasks demonstrate that the proposed method achieves competitive or better results in most evaluated metrics, while maintaining efficient inference under the considered task settings.