Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval
Not on the map. Why?
Abstract (arXiv)
Effective analysis of unusual domain specific video collections represents an important practical problem, where state-of-the-art general purpose models still face limitations. Hence, it is desirable to design benchmark datasets that challenge novel powerful models for specific domains with additional constraints. It is important to remember that domain specific data may be noisier (e.g., endoscopic or underwater videos) and often require more experienced users for effective search. In this paper, we focus on single-shot videos taken from moving cameras in underwater environments, which constitute a nontrivial challenge for research purposes. The first shard of a new Marine Video Kit dataset is presented to serve for video retrieval and other computer vision challenges. Our dataset is used in a special session during Video Browser Showdown 2023. In addition to basic meta-data statistics, we present several insights based on low-level features as well as semantic annotations of selected keyframes. The analysis also contains experiments showing limitations of respected general purpose models for retrieval. Our dataset and code are publicly available at https://hkust-vgd.github.io/marinevideokit.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 23 Sep 2022.
Builds on
8 of 24 references.
References · 8
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
- 2017Dense-Captioning Events in Videoscited by 789
- 2017Towards Automatic Learning of Procedures from Web Instructional Videoscited by 616
- 2021ClipCap: CLIP Prefix for Image Captioningcited by 557
- 2018A Joint Sequence Fusion Model for Video Question Answering and Retrievalcited by 208
- 2018Enhancing Underwater Imagery using Generative Adversarial Networkscited by 94
- 2018V3C - a Research Video Collectioncited by 12
Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 5.
9 papers.
- 2024A Survey of Video Datasets for Grounded Event Understandingcited by 9
- 2025Think Before You Segment: High-Quality Reasoning Segmentation with GPT Chain of Thoughtscited by 7
- 2024End-To-End Underwater Video Enhancement: Dataset and Modelcited by 5
- 2024UnDIVE: Generalized Underwater Video Enhancement Using Generative Priorscited by 3
- 2025AUTV: Creating Underwater Video Datasets with Pixel-wise Annotationscited by 1
- 2023MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understandingcited by 0
- 2025Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIPcited by 0
- 2025MSC: A Marine Wildlife Video Dataset with Grounded Segmentation and Clip-Level Captioningcited by 0
- 2025Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasetscited by 0
- 2025Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets15 Nov 2025
- 2025MSC: A Marine Wildlife Video Dataset with Grounded Segmentation and Clip-Level Captioning6 Aug 2025
- 2025Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP7 Jun 2025
- 2025AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations17 Mar 2025
- 2025Think Before You Segment: High-Quality Reasoning Segmentation with GPT Chain of Thoughts10 Mar 2025
- 2024UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors8 Nov 2024
- 2024A Survey of Video Datasets for Grounded Event Understanding14 Jun 2024
- 2024End-To-End Underwater Video Enhancement: Dataset and Model18 Mar 2024
- 2023MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding7 Jun 2023