  Automatic image cropping models predict reframing boxes to enhance image
aesthetics. Yet, the scarcity of labeled data hinders the progress of this
task. To overcome this limitation, we explore the possibility of utilizing both
labeled and unlabeled data together to expand the scale of training data for
image cropping models. This idea can be implemented in a pseudo-labeling way:
producing pseudo labels for unlabeled data by a teacher model and training a
student model with these pseudo labels. However, the student may learn from
teacher's mistakes. To address this issue, we propose the multi-policy
viewfinder network (MPV-Net) that offers diverse refining policies to rectify
the mistakes in original pseudo labels from the teacher. The most reliable
policy is selected to generate trusted pseudo labels. The reliability of
policies is evaluated via the robustness against box jittering. The efficacy of
our method can be evaluated by the improvement compared to the supervised
baseline which only uses labeled data. Notably, our MPV-Net outperforms
off-the-shelf pseudo-labeling methods, yielding the most substantial
improvement over the supervised baseline. Furthermore, our approach achieves
state-of-the-art results on both the FCDB and FLMS datasets, signifying the
superiority of our approach.
