Update README.md

README.md

@@ -46,7 +46,7 @@ dataset_summary: '
 
   # Note: other available arguments include ''max_samples'', etc
 
-  dataset = load_from_hub("harpreetsahota/WebUOT-238-Test")
+  dataset = load_from_hub("Voxel51/WebUOT-238-Test")
 
 
   # Launch the App
@@ -60,10 +60,6 @@ dataset_summary: '
 
 # Dataset Card for WebUOT-238-Test
 
-<!-- Provide a quick summary of the dataset. -->
-
-
-
 
 
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 238 samples.
@@ -84,141 +80,135 @@ from fiftyone.utils.huggingface import load_from_hub
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/WebUOT-238-Test")
+dataset = load_from_hub("Voxel51/WebUOT-238-Test")
 
 # Launch the App
 session = fo.launch_app(dataset)
 ```
 
 
-## Dataset Details
-
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
+WebUOT-1M is the largest million-scale benchmark for underwater object tracking (UOT), designed to address limitations in existing datasets by providing diverse underwater scenarios, rich annotations, and language prompts. It comprises **1.1 million frames** across **1,500 underwater videos**, covering **408 target categories** categorized into 12 superclasses (e.g., fish, molluscs, inanimate objects). The dataset includes high-quality bounding box annotations, 23 tracking attributes (e.g., illumination variation, camouflage), and language descriptions for multimodal tracking research.
 
+**Note:** This dataset, which has been parsed into FiftyOne format, comprises 238 randomly selected videos from the WebUOT-1M test set for a total of 192,000+ frames.
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
+### Dataset Details
 
-### Dataset Sources [optional]
+- **Curated by:**  
+  Chunhui Zhang (Shanghai Jiao Tong University), Li Liu (HKUST-Guangzhou), Guanjie Huang (HKUST-Guangzhou), Hao Wen (CloudWalk), Xi Zhou (CloudWalk), Yanfeng Wang (Shanghai Jiao Tong University).  
+- **Funded by:**  
+  National Natural Science Foundation of China (No. 62101351), Key R&D Program of Chongqing (cstc2021jscx-gksbX0032).  
+- **Language(s):**  
+  English (annotations and language prompts).  
+- **License:**  
+  [Creative Commons (intended for academic research).](https://creativecommons.org/licenses/by/4.0/)
+- **Shared by:** [Harpreet Sahota, Hacker-in-Residence @ Voxel51](https://huggingface.co/harpreetsahota)
 
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Dataset Sources
+- **Repository:** https://github.com/983632847/Awesome-Multimodal-Object-Tracking/tree/main/WebUOT-1M
+- **Paper:** https://arxiv.org/abs/2405.19818
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
+- Training/evaluating UOT algorithms.  
+- Multimodal tracking (vision + language prompts).  
+- Studying domain adaptation (underwater vs. open-air environments).  
+- Marine conservation, underwater robotics, and search/rescue applications.  
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+- Non-underwater tracking tasks (e.g., aerial/terrestrial tracking).  
+- Commercial applications without proper licensing.  
+- Non-visual tasks (e.g., audio analysis).  
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+- **Fields:**  
+  - Videos: 1,500 clips (1,020 train / 480 test).  
+  - Annotations: Bounding boxes, absent labels, 23 attributes (e.g., low visibility, similar distractors).  
+  - Language Prompts: Text descriptions of targets (e.g., "red clownfish in yellow coral").  
+  - Metadata: Object categories (408), superclasses (12), resolution, duration.  
+- **Splits:**  
+  Train/Test sets divided by videos, ensuring no overlap in categories or scenarios.  
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+To bridge the gap in UOT research caused by small-scale datasets, WebUOT-1M was created to enable robust model training/evaluation, domain adaptation, and multimodal tracking in complex underwater environments.  
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
+- **Sources:** YouTube, Bilibili (filtered for diversity).  
+- **Processing:**  
+  - Manual selection of moving targets.  
+  - Semi-supervised enhancement for blurry/low-visibility frames.  
+  - Professional annotation team for bounding boxes and attributes.  
+  - Final verification by authors.  
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+#### Who are the source data producers?  
 
-[More Information Needed]
+Videos were captured by divers, underwater robots, and hobbyists using varied devices (cameras, phones).  
 
-#### Who are the annotators?
+### Annotations
 
-<!-- This section describes the people or systems who created the annotations. -->
+#### Annotation Process  
 
-[More Information Needed]
+- **Tools:** In-house annotation tools; enhanced frames for challenging cases.  
+- **Guidelines:** Focus on target motion, bounding box accuracy, and attribute labeling (23 attributes).  
+- **Validation:** Multiple rounds of correction by authors.  
 
-#### Personal and Sensitive Information
+#### Who are the annotators?  
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+A professional labeling team and the authors performed verification.
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+```bibtex
+@article{zhang2024webuot,
+  title={WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark},
+  author={Zhang, Chunhui and Liu, Li and Huang, Guanjie and Wen, Hao and Zhou, Xi and Wang, Yanfeng},
+  journal={arXiv preprint arXiv:2405.19818},
+  year={2024}
+}
+```
 
-## Dataset Card Contact
 
-[More Information Needed]
+## Glossary
+
+The following glossary details the attributes of each video.
+
+Here's the content parsed as a markdown table:
+
+| Attribute | Definition |
+|-----------|------------|
+| 01. LR | If the size of the bounding box of the target in one frame is less than 400 pixels. |
+| 02. FM | The center position of the target in two consecutive frames exceeds 20 pixels. |
+| 03. SV | The ratio of the target bounding box is not within the range [0.5, 2]. |
+| 04. ARV | The aspect ratio of the target bounding box is not in the range [0.5, 2]. |
+| 05. CM | There is severe camera movement in the video frame. |
+| 06. VC | Viewpoint changes significantly affect the appearance of the target. |
+| 07. PO | If the target appears partially occluded in one frame. |
+| 08. FO | As long as the target is completely occluded in one frame. |
+| 09. OV | There is one frame where the target completely leaves the video frame. |
+| 10. ROT | The target rotates in the video frame. |
+| 11. DEF | The target appears deformation in the video frame. |
+| 12. SD | Similarity interference appears around the target. |
+| 13. IV | The illumination of the target area changes significantly. |
+| 14. MB | The target area becomes blurred due to target motion or camera motion. |
+| 15. PTI | In the initial frame only partial information about the target is visible. |
+| 16. NAO | The target belongs to a natural or artificial object. |
+| 17. CAM | The target is camouflaging in the video frame. |
+| 18. UV | The underwater visibility of the target area (low, medium, or high visibility). |
+| 19. WCV | The color of the water of the target area. |
+| 20. US | Different underwater scenarios where the target is located. |
+| 21. SP | Different shooting perspectives (underwater, outside-water, and fish-eye views). |
+| 22. SIZ | The size s = √(w × h) of the video is small (s < √(640 × 480)), medium (√(640 × 480) ≤ s < √(1280 × 720)), or large (s ≥ √(1280 × 720)). |
+| 23. LEN | The length l of the video is short (l ≤ 600 frames), medium (600 frames < l ≤ 1800 frames), or long (l > 1800 frames). |