New Variants of Frank-Wolfe Algorithm for Video Co-localization Problem
This work addresses the co-localization problem for video analysis, but it appears incremental as it builds on existing methods like conditional gradient sliding.
The authors tackled the video co-localization problem by proposing new variants of the Frank-Wolfe algorithm, demonstrating efficiency through a faster rate of decrease in the Wolfe gap in numerical experiments on the YouTube-Objects dataset.
The co-localization problem is a model that simultaneously localizes objects of the same class within a series of images or videos. In \cite{joulin2014efficient}, authors present new variants of the Frank-Wolfe algorithm (aka conditional gradient) that increase the efficiency in solving the image and video co-localization problems. The authors show the efficiency of their methods with the rate of decrease in a value called the Wolfe gap in each iteration of the algorithm. In this project, inspired by the conditional gradient sliding algorithm (CGS) \cite{CGS:Lan}, We propose algorithms for solving such problems and demonstrate the efficiency of the proposed algorithms through numerical experiments. The efficiency of these methods with respect to the Wolfe gap is compared with implementing them on the YouTube-Objects dataset for videos.