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Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which faces difficulties in some complex scenarios. To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information. Taking the advantages of RGB and RGBD methods, we propose a novel depth-aware salient object detection framework, which has following superior designs: 1) It does not rely on depth data in the testing phase. 2) It comprehensively optimizes SOD features with multi-level depth-aware regularizations. 3) The depth information also serves as error-weighted map to correct the segmentation process. With these insightful designs combined, we make the first attempt in realizing an unified depth-aware framework with only RGB information as input for inference, which not only surpasses the state-of-the-art performance on five public RGB SOD benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a large margin, while adopting less information and implementation light-weighted. CCS CONCEPTS • Computing methodologies → Interest point and salient region detections.

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We are developing a compiler that translates ordinary MATLAB scripts into code suitable for compilation and execution on parallel computers supporting C and the MPI message-passing library. In this paper we report the speedup achieved for several MATLAB scripts on three diverse parallel architectures: a distributed-memory multicomputer (Meiko CS-2), a symmetric multiprocessor (Sun Enterprise Server 4000), and a cluster of symmetric multiprocessors (Sun SPARCserver 20s). By generating code suitable for execution on parallel computers, our system multiplies the gains achievable by compiling, rather than interpreting, MATLAB scripts. Generating parallel code has an additional advantage: the amount of primary memory available on most parallel computers makes it possible to solve problems too large to solve on a single workstation.