# Video understanding

V-JEPA 2 is a cutting-edge video understanding model developed by the FAIR team under Meta. It extends the pre-training objectives of VJEPA and has industry-leading video understanding capabilities.

InternVL3 is an advanced multimodal large language model series with powerful multimodal perception and reasoning capabilities, supporting image, video, and text inputs.

InternVL3-2B is a multimodal large language model implemented based on the Hugging Face Transformers library. It performs excellently in multimodal tasks such as image, video, and text processing, supporting multiple input methods and efficient batch inference.

SmolVLM2-2.2B is a lightweight multimodal model designed for analyzing video content. It can process video, image, and text inputs and generate text outputs.

xGen-MM-Vid (BLIP-3-Video) is an efficient and compact vision-language model equipped with an explicit temporal encoder, specifically designed to understand video content.

A video understanding model fine-tuned on an unknown dataset based on the MCG-NJU/videomae-base model, with an accuracy of 67.13%