# Multimodal Video Understanding

Cosmos-Reason1 is a physics AI model developed by NVIDIA, capable of understanding physical common sense and generating embodied decision-making natural language through long-chain reasoning.

Cosmos-Reason1 is a physical AI model developed by NVIDIA, capable of understanding physical common sense and generating embodied decisions through long-chain reasoning.

A fine-tuned version based on the lmms-lab/llava-onevision-qwen2-7b-ov model, supporting video-text-to-text conversion tasks.

InternVideo2 is a multimodal video understanding model with 6B parameters, focusing on video content analysis and comprehension tasks.

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring enhanced visual understanding, agent capabilities, and structured output generation.

The visual encoder for VideoLLaMA3, utilizing Arbitrary Resolution Visual Tokenization (AVT) technology to dynamically process images and videos of different resolutions.

VideoLLaMA2.1 is an upgraded version of VideoLLaMA2, focusing on enhancing spatiotemporal modeling and audio understanding capabilities in large video-language models.

VideoLLaMA 2 is a multimodal large language model focused on video understanding, equipped with spatiotemporal modeling and audio comprehension capabilities.

VideoLLaMA 2 is a multimodal large language model focused on video understanding and spatio-temporal modeling, supporting video and image inputs, capable of performing visual question answering and dialogue tasks.

Tarsier-34b is an open-source large-scale video-language model focused on generating high-quality video captions and achieving leading results in multiple public benchmarks.

VideoLLaMA 2 is a next-generation video large language model, focusing on enhancing spatiotemporal modeling and audio understanding capabilities, supporting multimodal video question answering and description tasks.

VideoLLaMA 2 is a multimodal large language model focused on video understanding and audio processing, capable of handling video and image inputs to generate natural language responses.

LLaVA-NeXT-Video is an open-source multimodal chatbot trained on mixed video and image data, excelling in video understanding capabilities.

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.

ShareGPT4Video-8B is an open-source video chatbot, fine-tuned on open-source video instruction data.

X-CLIP is an extension of CLIP for general video-language understanding, supporting zero-shot, few-shot, or fully supervised video classification, as well as video-text retrieval tasks.

X-CLIP is an extended version of CLIP for general video-language understanding, trained via contrastive learning on (video, text) pairs.

X-CLIP is an extension of CLIP for general video-language understanding, trained via contrastive learning on (video, text) pairs.

X-CLIP is a minimalist extension of CLIP for general video-language understanding, trained via contrastive learning on (video, text) pairs.

X-CLIP is an extended version of CLIP for general video-language understanding, trained on video-text pairs via contrastive learning, suitable for tasks like video classification and video-text retrieval.

X-CLIP is an extended version of CLIP for general video-language understanding, trained on (video, text) pairs via contrastive learning, suitable for tasks like video classification and video-text retrieval.