论文标题
通过细心的多任务转移学习的视觉兴趣预测
Visual Interest Prediction with Attentive Multi-Task Transfer Learning
论文作者
论文摘要
视觉兴趣和影响预测是计算机视觉领域的一个非常有趣的研究领域。在本文中，我们提出了一个基于转移学习和注意机制的神经网络模型，以预测数字照片中的视觉兴趣和情感维度。通过多任务学习框架来解决多维影响。通过各种实验，我们显示了所提出的方法的有效性。在基准数据集上对我们模型的评估显示出比当前最新系统的巨大改进。
Visual interest & affect prediction is a very interesting area of research in the area of computer vision. In this paper, we propose a transfer learning and attention mechanism based neural network model to predict visual interest & affective dimensions in digital photos. Learning the multi-dimensional affects is addressed through a multi-task learning framework. With various experiments we show the effectiveness of the proposed approach. Evaluation of our model on the benchmark dataset shows large improvement over current state-of-the-art systems.
