Vcgbench Diverse On Videoinstruct
Métriques
Consistency
Contextual Understanding
Correctness of Information
Dense Captioning
Detail Orientation
Reasoning
Spatial Understanding
Temporal Understanding
mean
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Consistency | Contextual Understanding | Correctness of Information | Dense Captioning | Detail Orientation | Reasoning | Spatial Understanding | Temporal Understanding | mean | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BT-Adapter | 2.27 | 2.59 | 2.20 | 1.03 | 2.62 | 3.62 | 2.35 | 1.29 | 2.19 | BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning | |
| VideoGPT+ | 2.59 | 2.81 | 2.46 | 1.38 | 2.73 | 3.63 | 2.80 | 1.78 | 2.47 | VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding | |
| Chat-UniVi | 2.36 | 2.66 | 2.29 | 1.33 | 2.56 | 3.59 | 2.36 | 1.56 | 2.29 | Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding | |
| VideoChat2 | 2.27 | 2.51 | 2.13 | 1.26 | 2.42 | 3.13 | 2.43 | 1.66 | 2.20 | MVBench: A Comprehensive Multi-modal Video Understanding Benchmark | |
| VTimeLLM | 2.35 | 2.48 | 2.16 | 1.13 | 2.41 | 3.45 | 2.29 | 1.46 | 2.17 | VTimeLLM: Empower LLM to Grasp Video Moments | |
| Video-ChatGPT | 2.06 | 2.46 | 2.07 | 0.89 | 2.42 | 3.60 | 2.25 | 1.39 | 2.08 | Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models |
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