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Light field salient object detection (SOD) is an emerging research directionattributed to the richness of light field data. However, most existing methodslack effective handling of focal stacks, therefore making the latter involvedin a lot of interfering information and degrade the performance of SOD. Toaddress this limitation, we propose to utilize multi-modal features to refinefocal stacks in a guided manner, resulting in a novel guided focal stackrefinement network called GFRNet. To this end, we propose a guided refinementand fusion module (GRFM) to refine focal stacks and aggregate multi-modalfeatures. In GRFM, all-in-focus (AiF) and depth modalities are utilized torefine focal stacks separately, leading to two novel sub-modules for differentmodalities, namely AiF-based refinement module (ARM) and depth-based refinementmodule (DRM). Such refinement modules enhance structural and positionalinformation of salient objects in focal stacks, and are able to improve SODaccuracy. Experimental results on four benchmark datasets demonstrate thesuperiority of our GFRNet model against 12 state-of-the-art models.

Guided Focal Stack Refinement Network for Light Field Salient Object Detection