论文标题
突出对象检测是否真的需要深度？
Is Depth Really Necessary for Salient Object Detection?
论文作者
论文摘要
显着对象检测（SOD）是许多计算机视觉应用程序的至关重要和初步任务，这些应用程序已通过Deep CNN取得了进步。大多数现有方法主要依赖RGB信息来区分显着对象，这在某些复杂的情况下面临困难。为了解决这个问题，通过采用深度图作为独立输入并将功能与RGB信息融合在一起，提出了许多基于RGBD的网络。采用RGB和RGBD方法的优点，我们提出了一个新颖的深度感知的显着对象检测框架，该框架具有以下卓越的设计：1）仅在训练数据中仅依赖于测试阶段的RGB信息。 2）它全面优化了具有多层深度觉醒的正规化的SOD特征。 3）深度信息还用作错误加权图以纠正分割过程。凭借这些洞察力的设计，我们首次尝试实现一个统一的深度感知框架，仅使用RGB信息作为推理的输入，这不仅超过了在五个公共RGB SOD基准上的最先进的表演，而且还超过了五个基于RGBD的方法，在五个基于RGBD的方法上，在五个基准的方法上通过大量的元素进行了少量信息和实现。代码和模型将公开可用。
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 only takes the depth information as training data while only relies on RGB information 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 performances 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. The code and model will be publicly available.
