论文标题
DUT-LFSALITION：多功能数据集和光场到RGB显着检测
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection
论文作者
论文摘要
光场数据具有有利于显着性检测的有利特征。基于学习的光场显着性检测的成功在很大程度上取决于如何为更高的模型概括，可以有效利用高维光场数据以及如何设计灵活模型以实现台式计算机和移动设备的多功能性。要回答这些问题，首先，我们引入了一个大型数据集，以启用RGB，RGB-D和光场显着性检测的多功能应用程序，其中包含102个类和4204个样本。其次，我们提出了一个不对称的两流模型，该模型由焦点和RGB流组成。该焦点旨在在台式计算机上实现更高的性能，并依靠两个量身定制的模块将重点知识转移到RGB流。 RGB流通过三个蒸馏方案保证移动设备上的灵活性和内存/计算效率。实验表明，我们的焦点可以达到最新的性能。与最佳性能方法相比，RGB流对DUTLF-V2达到了TOP-2 F量级，该dutlf-v2可将模型尺寸降低83％，并将FPS的fps提高5倍。此外，我们提出的蒸馏计划适用于RGB显着性模型，在确保灵活性的同时，获得了令人印象深刻的性能。
Light field data exhibit favorable characteristics conducive to saliency detection. The success of learning-based light field saliency detection is heavily dependent on how a comprehensive dataset can be constructed for higher generalizability of models, how high dimensional light field data can be effectively exploited, and how a flexible model can be designed to achieve versatility for desktop computers and mobile devices. To answer these questions, first we introduce a large-scale dataset to enable versatile applications for RGB, RGB-D and light field saliency detection, containing 102 classes and 4204 samples. Second, we present an asymmetrical two-stream model consisting of the Focal stream and RGB stream. The Focal stream is designed to achieve higher performance on desktop computers and transfer focusness knowledge to the RGB stream, relying on two tailor-made modules. The RGB stream guarantees the flexibility and memory/computation efficiency on mobile devices through three distillation schemes. Experiments demonstrate that our Focal stream achieves state-of-the-arts performance. The RGB stream achieves Top-2 F-measure on DUTLF-V2, which tremendously minimizes the model size by 83% and boosts FPS by 5 times, compared with the best performing method. Furthermore, our proposed distillation schemes are applicable to RGB saliency models, achieving impressive performance gains while ensuring flexibility.
