Papers › Inverse Compositional Spatial Transformer Networks

Inverse Compositional Spatial Transformer Networks

12 Dec 2016CVPR 2017 7arXiv:1612.03897archive 2025-07-28

Chen-Hsuan Lin, Simon Lucey

In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs). STNs are of interest to the vision and learning communities due to their natural ability to combine alignment and classification within the same theoretical framework. Inspired by the Inverse Compositional (IC) variant of the LK algorithm, we present Inverse Compositional Spatial Transformer Networks (IC-STNs). We demonstrate that IC-STNs can achieve better performance than conventional STNs with less model capacity; in particular, we show superior performance in pure image alignment tasks as well as joint alignment/classification problems on real-world problems.

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