Benchmark Category
multimodal
11 public benchmarks in the multimodal category for video pricing and leaderboard research.
Benchmark
CharadesSTA
Charades-STA is a benchmark dataset for temporal activity localization via language queries, extending the Charades dataset with sentence temporal annotations. It contains 12,408 training and 3,720 testing segment-sentence pairs from videos with natural language descriptions and precise temporal boundaries for localizing activities based on language queries.
Benchmark
MLVU
A comprehensive benchmark for multi-task long video understanding that evaluates multimodal large language models on videos ranging from 3 minutes to 2 hours across 9 distinct tasks including reasoning, captioning, recognition, and summarization.
Benchmark
MMBench-Video
A long-form multi-shot benchmark for holistic video understanding that incorporates approximately 600 web videos from YouTube spanning 16 major categories, with each video ranging from 30 seconds to 6 minutes. Includes roughly 2,000 original question-answer pairs covering 26 fine-grained capabilities.
Benchmark
MMVU
MMVU (Multimodal Multi-disciplinary Video Understanding) is a benchmark for evaluating multimodal models on video understanding tasks across multiple disciplines, testing comprehension and reasoning capabilities on video content.
Benchmark
MVBench
A comprehensive multi-modal video understanding benchmark covering 20 challenging video tasks that require temporal understanding beyond single-frame analysis. Tasks span from perception to cognition, including action recognition, temporal reasoning, spatial reasoning, object interaction, scene transition, and counterfactual inference. Uses a novel static-to-dynamic method to systematically generate video tasks from existing annotations.
Benchmark
MotionBench
MotionBench is a benchmark for evaluating multimodal models on motion understanding in videos, testing the ability to comprehend temporal dynamics, movement patterns, and action sequences.
Benchmark
PerceptionTest
A novel multimodal video benchmark designed to evaluate perception and reasoning skills of pre-trained models across video, audio, and text modalities. Contains 11.6k real-world videos (average 23 seconds) filmed by participants worldwide, densely annotated with six types of labels. Focuses on skills (Memory, Abstraction, Physics, Semantics) and reasoning types (descriptive, explanatory, predictive, counterfactual). Shows significant performance gap between human baseline (91.4%) and state-of-the-art video QA models (46.2%).
Benchmark
VATEX
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research. Contains over 41,250 videos and 825,000 captions in both English and Chinese, with over 206,000 English-Chinese parallel translation pairs. Supports multilingual video captioning and video-guided machine translation tasks.
Benchmark
Video-MME (long, no subtitles)
Video-MME is the first-ever comprehensive evaluation benchmark for Multi-modal Large Language Models (MLLMs) in video analysis. This variant focuses on long-term videos (30min-60min) without subtitle inputs, testing robust contextual dynamics across 6 primary visual domains with 30 subfields including knowledge, film & television, sports competition, life record, and multilingual content.
Benchmark
VideoMME w sub.
The first-ever comprehensive evaluation benchmark of Multi-modal LLMs in Video analysis. Features 900 videos (254 hours) with 2,700 question-answer pairs covering 6 primary visual domains and 30 subfields. Evaluates temporal understanding across short (11 seconds) to long (1 hour) videos with multi-modal inputs including video frames, subtitles, and audio.
Benchmark
VideoMME w/o sub.
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
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