# CoralVOS: Dataset and Benchmark for Coral Video Segmentation

arXiv: https://arxiv.org/abs/2310.01946 · HTML: https://mlsanity.com/paper/2310.01946/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Track Anything: Segment Anything Meets Videos](https://mlsanity.com/paper/2304.11968/) (2023)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [Tracking Anything with Decoupled Video Segmentation](https://mlsanity.com/paper/2309.03903/) (2023)
- [Segment and Track Anything](https://mlsanity.com/paper/2305.06558/) (2023)
- [Decoupling Features in Hierarchical Propagation for Video Object Segmentation](https://mlsanity.com/paper/2210.09782/) (2022)
- [End-to-End Referring Video Object Segmentation with Multimodal Transformers](https://mlsanity.com/paper/2111.14821/) (2021)
- [Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion](https://mlsanity.com/paper/2103.07941/) (2021)
- [Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration](https://mlsanity.com/paper/2010.06349/) (2020)
- [Maskomaly:Zero-Shot Mask Anomaly Segmentation](https://mlsanity.com/paper/2305.16972/) (2023)
- [Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting](https://mlsanity.com/paper/1810.07733/) (2018)
- [Detecting and Counting Oysters](https://mlsanity.com/paper/2105.09758/) (2021)
- [Meta Learning Deep Visual Words for Fast Video Object Segmentation](https://mlsanity.com/paper/1812.01397/) (2018)

## Built on by (most cited first, 9 of 9)

- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study](https://mlsanity.com/paper/2401.02147/) (2024)
- [Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment](https://mlsanity.com/paper/2408.02924/) (2024)
- [AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis](https://mlsanity.com/paper/2502.01785/) (2025)
- [CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask](https://mlsanity.com/paper/2410.20436/) (2024)
- [AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring](https://mlsanity.com/paper/2509.01878/) (2025)
- [ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition](https://mlsanity.com/paper/2510.16822/) (2025)
- [Human-in-the-Loop Segmentation of Multi-species Coral Imagery](https://mlsanity.com/paper/2404.09406/) (2024)
- [Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images](https://mlsanity.com/paper/2606.23653/) (2026)

## Abstract (arXiv)

Coral reefs formulate the most valuable and productive marine ecosystems, providing habitat for many marine species. Coral reef surveying and analysis are currently confined to coral experts who invest substantial effort in generating comprehensive and dependable reports (e.g., coral coverage, population, spatial distribution, textitetc), from the collected survey data. However, performing dense coral analysis based on manual efforts is significantly time-consuming, the existing coral analysis algorithms compromise and opt for performing down-sampling and only conducting sparse point-based coral analysis within selected frames. However, such down-sampling will inevitable introduce the estimation bias or even lead to wrong results. To address this issue, we propose to perform dense coral video segmentation, with no down-sampling involved. Through video object segmentation, we could generate more textitreliable and textitin-depth coral analysis than the existing coral reef analysis algorithms. To boost such dense coral analysis, we propose a large-scale coral video segmentation dataset: CoralVOS as demonstrated in Fig. 1. To the best of our knowledge, our CoralVOS is the first dataset and benchmark supporting dense coral video segmentation. We perform experiments on our CoralVOS dataset, including 6 recent state-of-the-art video object segmentation (VOS) algorithms. We fine-tuned these VOS algorithms on our CoralVOS dataset and achieved observable performance improvement. The results show that there is still great potential for further promoting the segmentation accuracy. The dataset and trained models will be released with the acceptance of this work to foster the coral reef research community.