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SceneBench

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@@ -7,9 +7,6 @@
 ## 👀 Overview
-<p align="center">
-    <img src="./assets/stats.png" width="80%" height="80%">
-</p>
 Long-video understanding remains challenging for multimodal large language models because real videos are not merely long sequences of frames, but are organized into semantically coherent scenes. Existing video benchmarks often emphasize short-clip perception or sparse frame matching, which makes it difficult to evaluate whether a model can understand scene-level events, connect multimodal cues across multi-minute videos, and reason over temporally distributed evidence.
@@ -27,6 +24,9 @@ We introduce **SceneBench**, a scene-aware long-video benchmark designed to eval
 * **Practical long-video evaluation**: The benchmark is designed for realistic long-video settings where simply increasing the number of input frames may introduce irrelevant visual noise or exceed model memory limits.
 ## License

 ## 👀 Overview
 Long-video understanding remains challenging for multimodal large language models because real videos are not merely long sequences of frames, but are organized into semantically coherent scenes. Existing video benchmarks often emphasize short-clip perception or sparse frame matching, which makes it difficult to evaluate whether a model can understand scene-level events, connect multimodal cues across multi-minute videos, and reason over temporally distributed evidence.
 * **Practical long-video evaluation**: The benchmark is designed for realistic long-video settings where simply increasing the number of input frames may introduce irrelevant visual noise or exceed model memory limits.
+<p align="center">
+    <img src="./assets/stats.png" width="80%" height="80%">
+</p>
 ## License