# The 2018 DAVIS Challenge on Video Object Segmentation

arXiv: https://arxiv.org/abs/1803.00557 · HTML: https://mlsanity.com/paper/1803.00557/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 30 of 47)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion](https://mlsanity.com/paper/2103.07941/) (2021)
- [Segment Anything Meets Point Tracking](https://mlsanity.com/paper/2307.01197/) (2023)
- [OMG-Seg: Is One Model Good Enough For All Segmentation?](https://mlsanity.com/paper/2401.10229/) (2024)
- [Flow-Guided Transformer for Video Inpainting](https://mlsanity.com/paper/2208.06768/) (2022)
- [Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation](https://mlsanity.com/paper/2412.06016/) (2024)
- [Progressive Temporal Feature Alignment Network for Video Inpainting](https://mlsanity.com/paper/2104.03507/) (2021)
- [HyperSeg: Towards Universal Visual Segmentation with Large Language Model](https://mlsanity.com/paper/2411.17606/) (2024)
- [Object Segmentation-Assisted Inter Prediction for Versatile Video Coding](https://mlsanity.com/paper/2403.11694/) (2024)
- [Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps](https://mlsanity.com/paper/2104.10386/) (2021)
- [Efficient Track Anything](https://mlsanity.com/paper/2411.18933/) (2024)
- [Error Compensation Framework for Flow-Guided Video Inpainting](https://mlsanity.com/paper/2207.10391/) (2022)
- [Learning the What and How of Annotation in Video Object Segmentation](https://mlsanity.com/paper/2311.04414/) (2023)
- [Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild](https://mlsanity.com/paper/2103.10391/) (2021)
- [Revealing Occlusions with 4D Neural Fields](https://mlsanity.com/paper/2204.10916/) (2022)
- [DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos](https://mlsanity.com/paper/2506.09997/) (2025)
- [Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2302.08641/) (2023)
- [ScribbleSeg: Scribble-based Interactive Image Segmentation](https://mlsanity.com/paper/2303.11320/) (2023)
- [Event-Free Moving Object Segmentation from Moving Ego Vehicle](https://mlsanity.com/paper/2305.00126/) (2023)
- [TRASE: Tracking-free 4D Segmentation and Editing](https://mlsanity.com/paper/2411.19290/) (2024)
- [Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge](https://mlsanity.com/paper/2111.07950/) (2021)
- [Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting](https://mlsanity.com/paper/2301.10048/) (2023)
- [Revisiting Click-based Interactive Video Object Segmentation](https://mlsanity.com/paper/2203.01784/) (2022)
- [Point-VOS: Pointing Up Video Object Segmentation](https://mlsanity.com/paper/2402.05917/) (2024)
- [What is Point Supervision Worth in Video Instance Segmentation?](https://mlsanity.com/paper/2404.01990/) (2024)
- [UVIS: Unsupervised Video Instance Segmentation](https://mlsanity.com/paper/2406.06908/) (2024)
- [Fast Interactive Video Object Segmentation with Graph Neural Networks](https://mlsanity.com/paper/2103.03821/) (2021)
- [Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation](https://mlsanity.com/paper/2408.00169/) (2024)

## Abstract (arXiv)

We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 2017 dataset, which was presented in the previous edition of the DAVIS Challenge, and added 100 videos with multiple objects per sequence to the original DAVIS 2016 dataset. Motivated by the analysis of the results of the 2017 edition, the main track of the competition will be the same than in the previous edition (segmentation given the full mask of the objects in the first frame -- semi-supervised scenario). This edition, however, also adds an interactive segmentation teaser track, where the participants will interact with a web service simulating the input of a human that provides scribbles to iteratively improve the result.