Datasets:

RTVBench
/

RTV-Bench

@@ -7,14 +7,14 @@ language:
 size_categories:
 - 1K<n<10K
 ---
-<div align="center">
   <h1>RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video</h1>
-</div>
-[![hf_checkpoint](https://img.shields.io/badge/🤗-RTV--Bench-9C276A.svg)](https://huggingface.co/datasets/xunsh/RTV-Bench)
 <!-- [![ms_checkpoint](https://img.shields.io/badge/🤖-RTV--Bench-8A2BE2.svg)](https://www.modelscope.cn/datasets/Jungang/RTV-Bench)   -->
-## 🔥 News
 * **`2025.05.03`** 🌟 We are happy to release the RTV-Bench.
 ## TODO
@@ -27,7 +27,7 @@ size_categories:
 We introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis, which contains **552** videos (167.2 hours) and **4,631** high-quality QA pairs. We evaluated leading MLLMs, including proprietary (GPT-4o, Gemini 2.0), open-source offline (Qwen2.5-VL, VideoLLaMA3), and open-source real-time (VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost $\mathcal{RTV}\text{-}Bench$ performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs.  $\mathcal{RTV}\text{-}Bench$ includes three key principles:
 * **Multi-Timestamp Question Answering (MTQA)**, where answers evolve with scene changes;
 * **Hierarchical Question Structure**, combining basic and advanced queries; and
-* **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning.
 <!-- ## 🌟 Star History

 size_categories:
 - 1K<n<10K
 ---
+<!-- <div align="center">
   <h1>RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video</h1>
+</div> -->
+<!-- [![hf_checkpoint](https://img.shields.io/badge/🤗-RTV--Bench-9C276A.svg)](https://huggingface.co/datasets/xunsh/RTV-Bench)  -->
 <!-- [![ms_checkpoint](https://img.shields.io/badge/🤖-RTV--Bench-8A2BE2.svg)](https://www.modelscope.cn/datasets/Jungang/RTV-Bench)   -->
+<!-- ## 🔥 News
 * **`2025.05.03`** 🌟 We are happy to release the RTV-Bench.
 ## TODO
 We introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis, which contains **552** videos (167.2 hours) and **4,631** high-quality QA pairs. We evaluated leading MLLMs, including proprietary (GPT-4o, Gemini 2.0), open-source offline (Qwen2.5-VL, VideoLLaMA3), and open-source real-time (VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost $\mathcal{RTV}\text{-}Bench$ performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs.  $\mathcal{RTV}\text{-}Bench$ includes three key principles:
 * **Multi-Timestamp Question Answering (MTQA)**, where answers evolve with scene changes;
 * **Hierarchical Question Structure**, combining basic and advanced queries; and
+* **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning.  -->
 <!-- ## 🌟 Star History