Update README.md

README.md

@@ -26,7 +26,7 @@ Video-MME applies to both **image MLLMs**, i.e., generalizing to multiple images
 
 ## 👀 Video-MME Overview
 
-In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements, but their potential in processing sequential visual data is still insufficiently explored. We introduce <strong>Video-MME</strong>, the first-ever full-spectrum, <strong>M</strong>ulti-<strong>M</strong>odal <strong>E</strong>valuation benchmark of MLLMs in <strong>Video</strong> analysis. It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. Video-MME comprises **900 videos** with a total of 256 hours, and **2,700 human-annotated question-answer pairs**. Our work distinguishes from existing benchmarks through four key features: 
+In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements, but their potential in processing sequential visual data is still insufficiently explored. We introduce <strong>Video-MME</strong>, the first-ever full-spectrum, <strong>M</strong>ulti-<strong>M</strong>odal <strong>E</strong>valuation benchmark of MLLMs in <strong>Video</strong> analysis. It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. Video-MME comprises **900 videos** with a total of 254 hours, and **2,700 human-annotated question-answer pairs**. Our work distinguishes from existing benchmarks through four key features: 
 * *Duration in temporal dimension*. Encompassing both **short- (< 2min)**, **medium- (4min\~15min)**, and **long-term (30min\~60min)** videos, ranging from **11 seconds to 1 hour**, for robust contextual dynamics;
 * *Diversity in video types*. Spanning **6 primary visual domains**, i.e., Knowledge, Film & Television, Sports Competition, Life Record, and Multilingual, with **30 subfields** to ensure broad scenario generalizability;
 * *Breadth in data modalities*. Integrating multi-modal inputs besides video frames, including **subtitles and audios**, to assess the all-round capabilities of MLLMs;