The Beijing ZhiYuan Artificial Intelligence Research Institute, in collaboration with Shanghai Jiao Tong University, Renmin University of China, Peking University, and Beijing University of Posts and Telecommunications, has launched an ultra-long video understanding model named Video-XL. This model is an important demonstration of the core capabilities of multimodal large models and a key step towards General Artificial Intelligence (AGI). Compared to existing multimodal large models, Video-XL shows superior performance and efficiency in processing long videos exceeding 10 minutes.

Shusheng · Puyu Lingbi (InternLM-XComposer) Version 2.5 was developed by the Shanghai Artificial Intelligence Laboratory, focusing on long context input and output capabilities, operating smoothly within a length of 96K, and trained with 24K interleaved image-text data. Key upgrades include: high-resolution image understanding, fine-grained video understanding, and multi-turn multi-image dialogue. In application, it can create web pages and write high-quality text-image articles. Evaluations show it surpasses state-of-the-art open-source models across 16 benchmark tests and performs at par with key tasks compared to GPT-4V and Gem.

In the world of AI, making machines understand videos is much harder than understanding images. Videos are dynamic, with sound, movement, and a myriad of complex scenes. In the past, AI viewed videos as if they were reading ancient scrolls, often leaving them baffled. But the introduction of VideoPrism might change everything. It's a video encoder developed by Google's research team that can achieve state-of-the-art levels on a variety of video understanding tasks with a single model. Whether it

Intelligent Spectrum AI has announced the open-source upgrade of the CogVLM2-Video model, a significant advancement in the field of video understanding. The CogVLM2-Video model addresses the limitations of existing video understanding models in handling missing temporal information by introducing multi-frame video images and timestamps as input to the encoder. Utilizing an automated method for constructing time localization data, it has generated 30,000 temporal-related video question and answer

Recently, Zhihua, Tsinghua University, and Peking University collaborated to launch a long video understanding benchmark project called LVBench. While existing multimodal large language models have made significant progress in understanding short videos, they still face challenges when dealing with long videos that can last for hours. LVBench was created to fill this gap.