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

The Indian language model BharatGPT is set to launch in April, supporting 11 local Indian languages. This model is jointly developed by a company owned by Asia's richest man, Mukesh Ambani, in collaboration with eight Indian universities. BharatGPT aims to address the complex language system in India, covering fields such as healthcare and finance. Industry experts believe its release will mark a significant milestone in India's autonomous AI technology capabilities. Ambani has previously collaborated with companies like NVIDIA to co-develop India's language AI models.

JUNLALA's AI software SUPERLALA officially launches in the Chinese market, emphasizing simplicity and the needs of Chinese users. The latest version of the JUNLALA App is released for the first time in China, featuring localized functionalities and interfaces. SUPERLALA is user-centric, offering a variety of features to ensure a smooth user experience.

Byte's large model BuboGPT supports fine-grained multimodal joint understanding of text, images, and audio. By introducing a visual localization method, BuboGPT can accurately locate objects in images. Researchers have utilized a multimodal instruction tuning training scheme, achieving good results on multimodal tasks.