# Segmentation Free Object Discovery in Video

arXiv: https://arxiv.org/abs/1609.00221 · HTML: https://mlsanity.com/paper/1609.00221/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2016.

## Built on by (most cited first, 3 of 3)

- [Forecasting Future Instance Segmentation with Learned Optical Flow and Warping](https://mlsanity.com/paper/2211.08049/) (2022)
- [Fashion Recommendation Based on Style and Social Events](https://mlsanity.com/paper/2208.00725/) (2022)
- [Online Deep Clustering with Video Track Consistency](https://mlsanity.com/paper/2206.03086/) (2022)

## Abstract (arXiv)

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.