Segmentation Free Object Discovery in Video
It provides a general-purpose tool for unsupervised object discovery in videos, which is incremental as it extends existing image proposals to video without supervision.
The paper tackles unsupervised object discovery in video by generating spatio-temporal proposals (tracks) using bounding box spatial correlations with minimal visual content, achieving competitive results on YouTube Objects and ILSVRC-2015 VID datasets.
In this paper we present a simple yet effective approach to extend without supervision any object proposal from static images to videos. Unlike previous methods, these spatio-temporal proposals, to which we refer as tracks, are generated relying on little or no visual content by only exploiting bounding boxes spatial correlations through time. The tracks that we obtain are likely to represent objects and are a general-purpose tool to represent meaningful video content for a wide variety of tasks. For unannotated videos, tracks can be used to discover content without any supervision. As further contribution we also propose a novel and dataset-independent method to evaluate a generic object proposal based on the entropy of a classifier output response. We experiment on two competitive datasets, namely YouTube Objects and ILSVRC-2015 VID.