Learning to Complement: Rethinking Focal Selection for Light Field Salient Object Detection

Light field salient object detection benefits from complementary focal cues, but independent slice scoring can favor redundant observations and overlook slices that become informative when combined. Effective fusion further requires accounting for dependencies between the selected focal evidence and all-focus features. We propose a framework that integrates complementarity-aware focal selection with relation-conditioned state-space fusion. Specifically, Complementarity-Aware Focal Selection and Aggregation (CFSA) estimates the conditional prediction gains of individual slices and slice pairs given the accumulated evidence. Its pairwise look-ahead captures complementary combinations whose predictive value is underestimated by individual scoring. The estimated gains guide progressive evidence selection and aggregation, with adaptive stopping under an encoding budget. Relation-Conditioned State-Space Fusion (RCSF) then models all-focus and focal features as observations of a shared spatial state. Cross-modal relations determine the observation mappings and weights in a regularized joint correction. The resulting closed-form update jointly incorporates the observation residuals while accounting for overlap between observation directions, allowing cross-modal dependencies to shape state correction and subsequent spatial propagation. Extensive experiments on four public light field salient object detection benchmarks demonstrate that the proposed method outperforms state-of-the-art approaches.