MidRank: Learning to rank based on subsequences
This work addresses ranking challenges in image applications, but it appears incremental as it builds on existing methods by using sub-sequences rather than introducing a new paradigm.
The authors tackled the problem of supervised learning to rank for image sequences by proposing MidRank, which learns from moderately sized sub-sequences instead of pairs or full sequences, resulting in improved ranking accuracy across various image ranking applications and datasets.
We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analyzing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequences instead. These sub-sequences contain useful structural ranking information that leads to better learnability during training and better generalization during testing. By exploiting sub-sequences, the proposed MidRank improves ranking accuracy considerably on an extensive array of image ranking applications and datasets.