Update README.md

README.md

@@ -43,19 +43,11 @@ This repo contains the annotation data for the paper "[MomentSeeker: A Comprehen
 ## 🔔 News:
 - 🥳 2025/03/07: We have released the MomentSeeker [Benchmark](https://huggingface.co/datasets/avery00/MomentSeeker) and [Paper](https://arxiv.org/abs/2502.12558)! 🔥
 
-## License
-Our dataset is under the CC-BY-NC-SA-4.0 license.
-
-⚠️ If you need to access and use our dataset, you must understand and agree: **This dataset is for research purposes only and cannot be used for any commercial or other purposes. The user assumes all effects arising from any other use and dissemination.**
-
-We do not own the copyright of any raw video files. Currently, we provide video access to researchers under the condition of acknowledging the above license. For the video data used, we respect and acknowledge any copyrights of the video authors. Therefore, for the movies, TV series, documentaries, and cartoons used in the dataset, we have reduced the resolution, clipped the length, adjusted dimensions, etc. of the original videos to minimize the impact on the rights of the original works. 
-
-If the original authors of the related works still believe that the videos should be removed, please contact hyyuan@ruc.edu.cn or directly raise an issue.
 
 
 ## Introduction
 
-We present MomentSeeker, a comprehensive benchmark to evaluate retrieval models' performance in handling general long-video moment retrieval (LVMR) tasks. MomentSeeker offers three key advantages. First, it incorporates long videos of over 500 seconds on average, making it the first benchmark specialized for long-video moment retrieval. Second, it covers a wide range of task categories (including Moment Search, Caption Alignment, Image-conditioned Moment Search, and Video-conditioned Moment Search) and diverse application scenarios (e.g., sports, movies, cartoons, and ego), making it a comprehensive tool for assessing retrieval models' general LVMR performance. Additionally, the evaluation tasks are carefully curated through human annotation, ensuring the reliability of assessment. We further fine-tune an MLLM-based LVMR retriever on synthetic data, which demonstrates strong performance on our benchmark. The checkpoint will release soon.
+We present MomentSeeker, a comprehensive benchmark to evaluate retrieval models' performance in handling general long-video moment retrieval (LVMR) tasks. MomentSeeker offers three key advantages. First, it incorporates long videos of over 500 seconds on average, making it the first benchmark specialized for long-video moment retrieval. Second, it covers a wide range of task categories (including Moment Search, Caption Alignment, Image-conditioned Moment Search, and Video-conditioned Moment Search) and diverse application scenarios (e.g., sports, movies, cartoons, and ego), making it a comprehensive tool for assessing retrieval models' general LVMR performance. Additionally, the evaluation tasks are carefully curated through human annotation, ensuring the reliability of assessment.
 
 
 
@@ -63,22 +55,6 @@ We present MomentSeeker, a comprehensive benchmark to evaluate retrieval models'
 
 
 
-
-## 🏆 Mini Leaderboard
-| Rank | Method                                    | Backbone         | # Params | CA      | MS      | IMS     | VMS     | Overall |
-|------|------------------------------------------|-----------------|---------|--------|--------|--------|--------|--------|
-| 1    | **V-Embedder**                            | InternVideo2-Chat| 8B       | <u>42.2</u> | **20.4** | **15.0** | **15.8** | **23.3** |
-| 2    | CoVR                                    | BLIP-Large       | 588M     | 25.8    | 17.4    | <u>12.3</u> | <u>12.3</u> | <u>17.1</u> |
-| 3    | InternVideo2                            | ViT              | 1B       | **44.6** | <u>18.2</u> | 4.8     | 0.0     | 16.9    |
-| 4    | MM-Ret                                  | CLIP-Base        | 149M     | 23.2    | 15.4    | 10.5    | 10.5    | 14.9    |
-| 5    | LanguageBind                            | CLIP-Large       | 428M     | 39.6    | 16.4    | 3.2     | 0.0     | 14.8    |
-| 6    | E5V                                     | LLaVA-1.6        | 8.4B     | 25.8    | 16.8    | 6.2     | 5.2     | 13.5    |
-| 7    | UniIR                                   | CLIP-Large       | 428M     | 25.0    | 15.2    | 6.4     | 0.0     | 10.9    |
-| 8    | MLLM2VEC                                | LLaVA-1.6        | 8.4B     | 6.4     | 6.2     | 3.0     | 3.0     | 4.7     |
-| 9    | MagicLens                               | CLIP-Large       | 428M     | 9.0     | 2.4     | 3.2     | 2.8     | 4.4     |
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 ## License
 Our dataset is under the CC-BY-NC-SA-4.0 license.
 
@@ -92,8 +68,6 @@ If the original authors of the related works still believe that the videos shoul
 ## Evaluation
 > Before you access our dataset, we kindly ask you to thoroughly read and understand the license outlined above. If you cannot agree to these terms, we request that you refrain from downloading our video data.
 
-The JSON file provides candidate videos for each question. The candidates can be ranked, and metrics such as Recall@1 and MAP@5 can be computed accordingly.
-
 
 
 ## Hosting and Maintenance