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Video Based Generative Performance 5

Metrics

gpt-score

Results

Performance results of various models on this benchmark

Model Name
gpt-score
Paper TitleRepository
PPLLaVA-7B3.21PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance-
Video Chat1.94VideoChat: Chat-Centric Video Understanding-
VideoChat22.66MVBench: A Comprehensive Multi-modal Video Understanding Benchmark-
SlowFast-LLaVA-34B2.77SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models-
LLaMA Adapter1.98LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model-
PLLaVA-34B2.67PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning-
BT-Adapter (zero-shot)2.13BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning-
Video LLaMA1.82Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding-
TS-LLaVA-34B2.77TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models-
MovieChat2.24MovieChat: From Dense Token to Sparse Memory for Long Video Understanding-
VTimeLLM2.49VTimeLLM: Empower LLM to Grasp Video Moments-
VideoGPT+2.83VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding-
Chat-UniVi2.39Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding-
BT-Adapter2.34BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning-
ST-LLM2.93ST-LLM: Large Language Models Are Effective Temporal Learners-
MiniGPT4-video-7B2.65MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens-
Video-ChatGPT1.98Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models-
VideoChat2_HD_mistral2.65MVBench: A Comprehensive Multi-modal Video Understanding Benchmark-
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