Summary of Webuot-1m: Advancing Deep Underwater Object Tracking with a Million-scale Benchmark, by Chunhui Zhang et al.
WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark
by Chunhui Zhang, Li Liu, Guanjie Huang, Hao Wen, Xi Zhou, Yanfeng Wang
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces WebUOT-1M, the largest public underwater object tracking (UOT) benchmark to date, comprising 1.1 million frames across 1,500 video clips from complex and realistic underwater environments. The dataset includes meticulous manual annotation and verification of bounding boxes for underwater targets, as well as language prompts for video sequences, expanding its application areas. The paper proposes a novel omni-knowledge distillation framework to transfer open-air domain knowledge to the UOT model through knowledge distillation, demonstrating effectiveness on both existing UOT datasets and WebUOT-1M. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates the largest public underwater object tracking (UOT) dataset, called WebUOT-1M. It has 1.1 million frames from complex underwater environments. The dataset helps train modern tracking algorithms by providing many examples of different things to track in the water. The researchers also create a way to teach open-air trackers how to work better in water using knowledge distillation. |
Keywords
» Artificial intelligence » Knowledge distillation » Object tracking » Tracking
