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Video Based Generative Performance 2

Metrics

gpt-score

Results

Performance results of various models on this benchmark

Paper Title
PPLLaVA-7B3.81PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
TS-LLaVA-34B3.69TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models
SlowFast-LLaVA-34B3.57SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
VideoGPT+3.39VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
PLLaVA-34B3.25PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
Chat-UniVi2.81Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
VideoChat22.81MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
ST-LLM2.81ST-LLM: Large Language Models Are Effective Temporal Learners
MiniGPT4-video-7B2.67MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
VideoChat2_HD_mistral2.62MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
VTimeLLM2.47VTimeLLM: Empower LLM to Grasp Video Moments
BT-Adapter2.46BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
MovieChat2.42MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
Video-ChatGPT2.37Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Video Chat2.24VideoChat: Chat-Centric Video Understanding
BT-Adapter (zero-shot)2.2BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
LLaMA Adapter2.15LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Video LLaMA1.79Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
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