Benchmark Category
vision
8 public benchmarks in the vision category for video pricing and leaderboard research.
Benchmark
ActivityNet
A large-scale video benchmark for human activity understanding. Provides samples from 203 activity classes with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 video hours. The benchmark covers a wide range of complex human activities that are of interest to people in their daily living and can be used to compare algorithms for three scenarios: untrimmed video classification, trimmed activity classification, and activity detection.
Benchmark
EgoSchema
A diagnostic benchmark for very long-form video language understanding consisting of over 5000 human curated multiple choice questions based on 3-minute video clips from Ego4D, covering a broad range of natural human activities and behaviors
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
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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