In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To reduce the error in labeling and solve the problem of normal data distribution, we propose a new aesthetic mixed dataset with classification and regression called AMD-CR, and we train a meta reweighting network to reweight the loss of training data differently. In addition, we provide a training strategy acccording to different stages, based on pseudo labels of the binary classification task, and then we use it for aesthetic training acccording to different stages in classification and regression tasks. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. Besides, we also use the efficient channel attention (ECA) to strengthen the feature extracting ability of each task. The experimental result shows that our method improves 0.1112 compared with the conventional methods in SROCC. The method can also help to find best aesthetic path planning for unmanned aerial vehicles (UAV) and vehicles.
翻译:在图像审美质量评估的任务中,由于正常的审美数据集分布,很难达到高分区和低分区。为减少标签错误和解决正常数据分布问题,我们提议了一个新的审美混合数据集,称为AMD-CR,并用分类和回归法进行分类和回归,我们培训了一个元重加权网络,以不同方式对培训数据损失进行重估。此外,我们根据二进制分类任务的假标签,提供了与不同阶段相匹配的培训战略,然后我们利用它进行与分类和回归任务不同阶段相匹配的美学培训。在网络结构建设中,我们建造了一个可适应任何大小输入图像的审美适应区结构。此外,我们还利用高效的频道关注(ECA)加强每项任务的特点提取能力。实验结果表明,我们的方法比SROCC的传统方法改进了0.1112。这种方法也有助于找到无人驾驶飞行器(UAVAV)和车辆的最佳审美路径规划。