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Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined.
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
Photo composition is an important factor affecting the aesthetics in photography.
Mask Editor allows drawing any bounding curve to mark objects and improves efficiency to mark objects with irregular shapes.
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.