# Video Question Answering

LLaVA-Video-7B-Qwen2-TPO is a video understanding model based on LLaVA-Video-7B-Qwen2 with temporal preference optimization, demonstrating excellent performance across multiple benchmarks.

mPLUG-Owl3 is an advanced multimodal large language model focused on addressing the challenges of long image sequence understanding, significantly improving processing speed and sequence length through the Hyper Attention mechanism.

VideoChat2-HD-hf is a multimodal video understanding model based on Mistral-7B, focusing on video-to-text conversion tasks.

Tarsier-7b is an open-source large-scale video-language model from the Tarsier series, specializing in generating high-quality video descriptions with excellent general video understanding capabilities.

CogVLM2-Video is a high-performance video understanding model that achieves state-of-the-art performance in multiple video question-answering tasks, capable of completing video understanding within one minute.

LLaVA-NeXT-Video is an open-source multimodal chatbot optimized through mixed training on video and image data, possessing excellent video understanding capabilities.

LLaVA-NeXT-Video is an open-source multimodal chatbot that achieves excellent video understanding capabilities through mixed training on video and image data, reaching SOTA level among open-source models on the VideoMME benchmark.

GIT is a dual-condition Transformer decoder based on CLIP image tokens and text tokens, specifically fine-tuned for the MSRVTT-QA task.

GIT is a Transformer decoder based on CLIP image tokens and text tokens for vision-language tasks.