# Large-Scale Object Mining for Object Discovery from Unlabeled Video

arXiv: https://arxiv.org/abs/1903.00362 · HTML: https://mlsanity.com/paper/1903.00362/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations](https://mlsanity.com/paper/2207.05027/) (2022)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [Better Call SAL: Towards Learning to Segment Anything in Lidar](https://mlsanity.com/paper/2403.13129/) (2024)
- [Tracking Every Thing in the Wild](https://mlsanity.com/paper/2207.12978/) (2022)
- [Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection](https://mlsanity.com/paper/2103.14198/) (2021)
- [The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory](https://mlsanity.com/paper/2105.01652/) (2021)
- [SeMoLi: What Moves Together Belongs Together](https://mlsanity.com/paper/2402.19463/) (2024)
- [Learning to Discover and Detect Objects](https://mlsanity.com/paper/2210.10774/) (2022)
- [Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos](https://mlsanity.com/paper/2104.12276/) (2021)
- [Lidar Panoptic Segmentation in an Open World](https://mlsanity.com/paper/2409.14273/) (2024)
- [Multi-Object Tracking with Hallucinated and Unlabeled Videos](https://mlsanity.com/paper/2108.08836/) (2021)

## Abstract (arXiv)

This paper addresses the problem of object discovery from unlabeled driving videos captured in a realistic automotive setting. Identifying recurring object categories in such raw video streams is a very challenging problem. Not only do object candidates first have to be localized in the input images, but many interesting object categories occur relatively infrequently. Object discovery will therefore have to deal with the difficulties of operating in the long tail of the object distribution. We demonstrate the feasibility of performing fully automatic object discovery in such a setting by mining object tracks using a generic object tracker. In order to facilitate further research in object discovery, we release a collection of more than 360,000 automatically mined object tracks from 10+ hours of video data (560,000 frames). We use this dataset to evaluate the suitability of different feature representations and clustering strategies for object discovery.